Мультиклассовое обнаружение неисправностей с использованием смоделированных данных

Этот пример использует:

Скрипт Open Live Script

В этом примере показано, как использовать модель Simulink, чтобы сгенерировать отказ и здоровые данные. Данные используются, чтобы разработать классификатор мультикласса, чтобы обнаружить различные комбинации отказов. Пример использует тройную модель поршневого насоса и включает утечку, блокирование и подшипник отказов.

Настройка модели

Этот пример использует много вспомогательных файлов, которые хранятся в zip-файле. Разархивируйте файл, чтобы получить доступ к вспомогательным файлам, загрузить параметры модели и создать библиотеку поршневого насоса. 

if ~exist('+mech_hydro_forcesPS','dir')
    unzip('pdmRecipPump_supportingfiles.zip')
end

% Load Parameters
pdmRecipPump_Parameters %Pump
CAT_Pump_1051_DataFile_imported %CAD

% Create Simscape library if needed
if exist('mech_hydro_forcesPS_Lib','file')~=4
    ssc_build mech_hydro_forcesPS
end
Generating Simulink library 'mech_hydro_forcesPS_lib' in the current directory 'C:\Users\anagar\OneDrive - MathWorks\Desktop\Predictive Project\RUL using DL\MultiFaultDetectionExample\ExampleManager\anagar.doc2\predmaint-ex92746726' ...

Модель поршневого насоса

Поршневой насос состоит из электродвигателя, корпуса насоса, кривошипа насоса и поршней насоса.

mdl = 'pdmRecipPump';
open_system(mdl)

open_system([mdl,'/Pump'])

Модель насоса сконфигурирована, чтобы смоделировать три типа отказов; цилиндрические утечки, блокированный вход и увеличенное трение подшипника. Эти отказы параметрируются как переменные рабочей области и конфигурируются через диалоговое окно блока насоса.

Симуляция отказа и здоровых данных

Поскольку каждый из трех типов отказа создает массив значений, которые представляют серьезность отказа, в пределах ни от какого отказа к значительному отказу.

% Define fault parameter variations
numParValues = 10;
leak_area_set_factor = linspace(0.00,0.036,numParValues);
leak_area_set = leak_area_set_factor*TRP_Par.Check_Valve.In.Max_Area;
leak_area_set = max(leak_area_set,1e-9); % Leakage area cannot be 0
blockinfactor_set = linspace(0.8,0.53,numParValues);
bearingfactor_set = linspace(0,6e-4,numParValues);

Модель насоса сконфигурирована, чтобы включать шум, таким образом запущение модели с теми же значениями параметров отказа приведет к различной симуляции выходные параметры. Это полезно для разработки классификатора, когда это означает, что может быть несколько результатов симуляции для того же условия отказа и серьезности. Чтобы конфигурировать моделирование для таких результатов, создайте векторы из значений параметров отказа, где значения не представляют отказов, одиночного отказа, комбинаций двух отказов и комбинаций трех отказов. Для каждой группы (никакой отказ, одиночный отказ, и т.д.) создают 125 комбинаций значений отказа от значений параметров отказа, заданных выше. Это дает в общей сложности 1 000 комбинаций значений параметров отказа. Обратите внимание на то, что выполнение этих 1 000 параллельных симуляций занимает приблизительно час на стандартном рабочем столе и генерирует приблизительно 620 МБ данных. Чтобы уменьшать время симуляции, сократите количество комбинаций отказа к 20 путем изменения runAll = true к runAll = false. Обратите внимание на то, что больший набор данных приводит к более устойчивому классификатору.

% Set number of elements in each fault group
runAll = true; 
if runAll
    % Create a large dataset to build a robust classifier
    nPerGroup = 100; 
else
    % Create a smaller dataset to reduce simulation time
    nPerGroup = 20; %#ok<UNRCH> 
end

rng('default');    % Feed default seed to rng (Random number generator)

% No fault simulations
leakArea = repmat(leak_area_set(1),nPerGroup,1);
blockingFactor = repmat(blockinfactor_set(1),nPerGroup,1);
bearingFactor = repmat(bearingfactor_set(1),nPerGroup,1);

% Single fault simulations
idx = ceil(10*rand(nPerGroup,1));
leakArea = [leakArea; leak_area_set(idx)'];
blockingFactor = [blockingFactor;repmat(blockinfactor_set(1),nPerGroup,1)];
bearingFactor = [bearingFactor;repmat(bearingfactor_set(1),nPerGroup,1)];
idx = ceil(10*rand(nPerGroup,1));
leakArea = [leakArea; repmat(leak_area_set(1),nPerGroup,1)];
blockingFactor = [blockingFactor;blockinfactor_set(idx)'];
bearingFactor = [bearingFactor;repmat(bearingfactor_set(1),nPerGroup,1)];
idx = ceil(10*rand(nPerGroup,1));
leakArea = [leakArea; repmat(leak_area_set(1),nPerGroup,1)];
blockingFactor = [blockingFactor;repmat(blockinfactor_set(1),nPerGroup,1)];
bearingFactor = [bearingFactor;bearingfactor_set(idx)'];

% Double fault simulations
idxA = ceil(10*rand(nPerGroup,1));
idxB = ceil(10*rand(nPerGroup,1));
leakArea = [leakArea; leak_area_set(idxA)'];
blockingFactor = [blockingFactor;blockinfactor_set(idxB)'];
bearingFactor = [bearingFactor;repmat(bearingfactor_set(1),nPerGroup,1)];
idxA = ceil(10*rand(nPerGroup,1));
idxB = ceil(10*rand(nPerGroup,1));
leakArea = [leakArea; leak_area_set(idxA)'];
blockingFactor = [blockingFactor;repmat(blockinfactor_set(1),nPerGroup,1)];
bearingFactor = [bearingFactor;bearingfactor_set(idxB)'];
idxA = ceil(10*rand(nPerGroup,1));
idxB = ceil(10*rand(nPerGroup,1));
leakArea = [leakArea; repmat(leak_area_set(1),nPerGroup,1)];
blockingFactor = [blockingFactor;blockinfactor_set(idxA)'];
bearingFactor = [bearingFactor;bearingfactor_set(idxB)'];

% Triple fault simulations
idxA = ceil(10*rand(nPerGroup,1));
idxB = ceil(10*rand(nPerGroup,1));
idxC = ceil(10*rand(nPerGroup,1));
leakArea = [leakArea; leak_area_set(idxA)'];
blockingFactor = [blockingFactor;blockinfactor_set(idxB)'];
bearingFactor = [bearingFactor;bearingfactor_set(idxC)'];

Используйте комбинации параметра отказа, чтобы создать Simulink.SimulationInput объекты. Для каждой симуляции вход гарантирует, что случайный seed собирается по-другому сгенерировать различные результаты.

for ct = numel(leakArea):-1:1
    simInput(ct) = Simulink.SimulationInput(mdl);
    simInput(ct) = setVariable(simInput(ct),'leak_cyl_area_WKSP',leakArea(ct));
    simInput(ct) = setVariable(simInput(ct),'block_in_factor_WKSP',blockingFactor(ct));
    simInput(ct) = setVariable(simInput(ct),'bearing_fault_frict_WKSP',bearingFactor(ct));
    simInput(ct) = setVariable(simInput(ct),'noise_seed_offset_WKSP',ct-1);
end

Используйте generateSimulationEnsemble функционируйте, чтобы запустить симуляции, заданные Simulink.SimulationInput объекты, заданные выше и хранилище результаты в локальной подпапке. Затем создайте simulationEnsembleDatastore от сохраненных результатов. 

% Run the simulation and create an ensemble to manage the simulation
% results

if isfolder('./Data')
    % Delete existing mat files
    delete('./Data/*.mat')
end   

[ok,e] = generateSimulationEnsemble(simInput,fullfile('.','Data'),'UseParallel',true);
[28-Jun-2021 13:38:31] Checking for availability of parallel pool...
Starting parallel pool (parpool) using the 'local' profile ...
Preserving jobs with IDs: 1 because they contain crash dump files.
You can use 'delete(myCluster.Jobs)' to remove all jobs created with profile local. To create 'myCluster' use 'myCluster = parcluster('local')'.
Connected to the parallel pool (number of workers: 6).
[28-Jun-2021 13:39:52] Starting Simulink on parallel workers...
[28-Jun-2021 13:41:17] Configuring simulation cache folder on parallel workers...
[28-Jun-2021 13:41:18] Transferring base workspace variables used in the model to parallel workers...
[28-Jun-2021 13:41:19] Loading model on parallel workers...
[28-Jun-2021 13:41:43] Running simulations...
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[28-Jun-2021 13:58:28] Completed 232 of 800 simulation runs
[28-Jun-2021 13:58:29] Completed 233 of 800 simulation runs
[28-Jun-2021 13:58:30] Completed 234 of 800 simulation runs
[28-Jun-2021 13:58:45] Completed 235 of 800 simulation runs
[28-Jun-2021 13:58:46] Completed 236 of 800 simulation runs
[28-Jun-2021 13:58:50] Completed 237 of 800 simulation runs
[28-Jun-2021 13:58:51] Completed 238 of 800 simulation runs
[28-Jun-2021 13:58:52] Completed 239 of 800 simulation runs
[28-Jun-2021 13:58:53] Completed 240 of 800 simulation runs
[28-Jun-2021 13:59:11] Completed 241 of 800 simulation runs
[28-Jun-2021 13:59:12] Completed 242 of 800 simulation runs
[28-Jun-2021 13:59:14] Completed 243 of 800 simulation runs
[28-Jun-2021 13:59:15] Completed 244 of 800 simulation runs
[28-Jun-2021 13:59:16] Completed 245 of 800 simulation runs
[28-Jun-2021 13:59:17] Completed 246 of 800 simulation runs
[28-Jun-2021 13:59:35] Completed 247 of 800 simulation runs
[28-Jun-2021 13:59:36] Completed 248 of 800 simulation runs
[28-Jun-2021 13:59:37] Completed 249 of 800 simulation runs
[28-Jun-2021 13:59:38] Completed 250 of 800 simulation runs
[28-Jun-2021 13:59:39] Completed 251 of 800 simulation runs
[28-Jun-2021 13:59:40] Completed 252 of 800 simulation runs
[28-Jun-2021 14:00:02] Completed 253 of 800 simulation runs
[28-Jun-2021 14:00:03] Completed 254 of 800 simulation runs
[28-Jun-2021 14:00:04] Completed 255 of 800 simulation runs
[28-Jun-2021 14:00:05] Completed 256 of 800 simulation runs
[28-Jun-2021 14:00:06] Completed 257 of 800 simulation runs
[28-Jun-2021 14:00:07] Completed 258 of 800 simulation runs
[28-Jun-2021 14:00:26] Completed 259 of 800 simulation runs
[28-Jun-2021 14:00:27] Completed 260 of 800 simulation runs
[28-Jun-2021 14:00:28] Completed 261 of 800 simulation runs
[28-Jun-2021 14:00:29] Completed 262 of 800 simulation runs
[28-Jun-2021 14:00:30] Completed 263 of 800 simulation runs
[28-Jun-2021 14:00:31] Completed 264 of 800 simulation runs
[28-Jun-2021 14:00:49] Completed 265 of 800 simulation runs
[28-Jun-2021 14:00:50] Completed 266 of 800 simulation runs
[28-Jun-2021 14:00:53] Completed 267 of 800 simulation runs
[28-Jun-2021 14:00:54] Completed 268 of 800 simulation runs
[28-Jun-2021 14:00:55] Completed 269 of 800 simulation runs
[28-Jun-2021 14:00:56] Completed 270 of 800 simulation runs
[28-Jun-2021 14:01:13] Completed 271 of 800 simulation runs
[28-Jun-2021 14:01:14] Completed 272 of 800 simulation runs
[28-Jun-2021 14:01:18] Completed 273 of 800 simulation runs
[28-Jun-2021 14:01:19] Completed 274 of 800 simulation runs
[28-Jun-2021 14:01:20] Completed 275 of 800 simulation runs
[28-Jun-2021 14:01:21] Completed 276 of 800 simulation runs
[28-Jun-2021 14:01:39] Completed 277 of 800 simulation runs
[28-Jun-2021 14:01:40] Completed 278 of 800 simulation runs
[28-Jun-2021 14:01:41] Completed 279 of 800 simulation runs
[28-Jun-2021 14:01:42] Completed 280 of 800 simulation runs
[28-Jun-2021 14:01:43] Completed 281 of 800 simulation runs
[28-Jun-2021 14:01:44] Completed 282 of 800 simulation runs
[28-Jun-2021 14:02:05] Completed 283 of 800 simulation runs
[28-Jun-2021 14:02:06] Completed 284 of 800 simulation runs
[28-Jun-2021 14:02:07] Completed 285 of 800 simulation runs
[28-Jun-2021 14:02:08] Completed 286 of 800 simulation runs
[28-Jun-2021 14:02:09] Completed 287 of 800 simulation runs
[28-Jun-2021 14:02:10] Completed 288 of 800 simulation runs
[28-Jun-2021 14:02:28] Completed 289 of 800 simulation runs
[28-Jun-2021 14:02:29] Completed 290 of 800 simulation runs
[28-Jun-2021 14:02:30] Completed 291 of 800 simulation runs
[28-Jun-2021 14:02:32] Completed 292 of 800 simulation runs
[28-Jun-2021 14:02:33] Completed 293 of 800 simulation runs
[28-Jun-2021 14:02:34] Completed 294 of 800 simulation runs
[28-Jun-2021 14:02:51] Completed 295 of 800 simulation runs
[28-Jun-2021 14:02:52] Completed 296 of 800 simulation runs
[28-Jun-2021 14:02:53] Completed 297 of 800 simulation runs
[28-Jun-2021 14:02:54] Completed 298 of 800 simulation runs
[28-Jun-2021 14:02:56] Completed 299 of 800 simulation runs
[28-Jun-2021 14:02:57] Completed 300 of 800 simulation runs
[28-Jun-2021 14:03:07] Completed 301 of 800 simulation runs
[28-Jun-2021 14:03:10] Completed 302 of 800 simulation runs
[28-Jun-2021 14:03:10] Completed 303 of 800 simulation runs
[28-Jun-2021 14:03:11] Completed 304 of 800 simulation runs
[28-Jun-2021 14:03:13] Completed 305 of 800 simulation runs
[28-Jun-2021 14:03:14] Completed 306 of 800 simulation runs
[28-Jun-2021 14:03:21] Completed 307 of 800 simulation runs
[28-Jun-2021 14:03:23] Completed 308 of 800 simulation runs
[28-Jun-2021 14:03:24] Completed 309 of 800 simulation runs
[28-Jun-2021 14:03:25] Completed 310 of 800 simulation runs
[28-Jun-2021 14:03:28] Completed 311 of 800 simulation runs
[28-Jun-2021 14:03:30] Completed 312 of 800 simulation runs
[28-Jun-2021 14:03:38] Completed 313 of 800 simulation runs
[28-Jun-2021 14:03:41] Completed 314 of 800 simulation runs
[28-Jun-2021 14:03:42] Completed 315 of 800 simulation runs
[28-Jun-2021 14:03:45] Completed 316 of 800 simulation runs
[28-Jun-2021 14:03:46] Completed 317 of 800 simulation runs
[28-Jun-2021 14:03:51] Completed 318 of 800 simulation runs
[28-Jun-2021 14:03:57] Completed 319 of 800 simulation runs
[28-Jun-2021 14:03:58] Completed 320 of 800 simulation runs
[28-Jun-2021 14:03:59] Completed 321 of 800 simulation runs
[28-Jun-2021 14:04:00] Completed 322 of 800 simulation runs
[28-Jun-2021 14:04:02] Completed 323 of 800 simulation runs
[28-Jun-2021 14:04:12] Completed 324 of 800 simulation runs
[28-Jun-2021 14:04:13] Completed 325 of 800 simulation runs
[28-Jun-2021 14:04:14] Completed 326 of 800 simulation runs
[28-Jun-2021 14:04:15] Completed 327 of 800 simulation runs
[28-Jun-2021 14:04:19] Completed 328 of 800 simulation runs
[28-Jun-2021 14:04:20] Completed 329 of 800 simulation runs
[28-Jun-2021 14:04:23] Completed 330 of 800 simulation runs
[28-Jun-2021 14:04:26] Completed 331 of 800 simulation runs
[28-Jun-2021 14:04:30] Completed 332 of 800 simulation runs
[28-Jun-2021 14:04:31] Completed 333 of 800 simulation runs
[28-Jun-2021 14:04:33] Completed 334 of 800 simulation runs
[28-Jun-2021 14:04:38] Completed 335 of 800 simulation runs
[28-Jun-2021 14:04:44] Completed 336 of 800 simulation runs
[28-Jun-2021 14:04:45] Completed 337 of 800 simulation runs
[28-Jun-2021 14:04:47] Completed 338 of 800 simulation runs
[28-Jun-2021 14:04:48] Completed 339 of 800 simulation runs
[28-Jun-2021 14:04:49] Completed 340 of 800 simulation runs
[28-Jun-2021 14:05:01] Completed 341 of 800 simulation runs
[28-Jun-2021 14:05:03] Completed 342 of 800 simulation runs
[28-Jun-2021 14:05:04] Completed 343 of 800 simulation runs
[28-Jun-2021 14:05:09] Completed 344 of 800 simulation runs
[28-Jun-2021 14:05:11] Completed 345 of 800 simulation runs
[28-Jun-2021 14:05:12] Completed 346 of 800 simulation runs
[28-Jun-2021 14:05:14] Completed 347 of 800 simulation runs
[28-Jun-2021 14:05:16] Completed 348 of 800 simulation runs
[28-Jun-2021 14:05:19] Completed 349 of 800 simulation runs
[28-Jun-2021 14:05:21] Completed 350 of 800 simulation runs
[28-Jun-2021 14:05:22] Completed 351 of 800 simulation runs
[28-Jun-2021 14:05:25] Completed 352 of 800 simulation runs
[28-Jun-2021 14:05:30] Completed 353 of 800 simulation runs
[28-Jun-2021 14:05:33] Completed 354 of 800 simulation runs
[28-Jun-2021 14:05:35] Completed 355 of 800 simulation runs
[28-Jun-2021 14:05:36] Completed 356 of 800 simulation runs
[28-Jun-2021 14:05:37] Completed 357 of 800 simulation runs
[28-Jun-2021 14:05:43] Completed 358 of 800 simulation runs
[28-Jun-2021 14:05:47] Completed 359 of 800 simulation runs
[28-Jun-2021 14:05:49] Completed 360 of 800 simulation runs
[28-Jun-2021 14:05:56] Completed 361 of 800 simulation runs
[28-Jun-2021 14:05:57] Completed 362 of 800 simulation runs
[28-Jun-2021 14:05:58] Completed 363 of 800 simulation runs
[28-Jun-2021 14:05:59] Completed 364 of 800 simulation runs
[28-Jun-2021 14:06:08] Completed 365 of 800 simulation runs
[28-Jun-2021 14:06:09] Completed 366 of 800 simulation runs
[28-Jun-2021 14:06:11] Completed 367 of 800 simulation runs
[28-Jun-2021 14:06:12] Completed 368 of 800 simulation runs
[28-Jun-2021 14:06:16] Completed 369 of 800 simulation runs
[28-Jun-2021 14:06:18] Completed 370 of 800 simulation runs
[28-Jun-2021 14:06:23] Completed 371 of 800 simulation runs
[28-Jun-2021 14:06:24] Completed 372 of 800 simulation runs
[28-Jun-2021 14:06:25] Completed 373 of 800 simulation runs
[28-Jun-2021 14:06:27] Completed 374 of 800 simulation runs
[28-Jun-2021 14:06:28] Completed 375 of 800 simulation runs
[28-Jun-2021 14:06:30] Completed 376 of 800 simulation runs
[28-Jun-2021 14:06:33] Completed 377 of 800 simulation runs
[28-Jun-2021 14:06:37] Completed 378 of 800 simulation runs
[28-Jun-2021 14:06:39] Completed 379 of 800 simulation runs
[28-Jun-2021 14:06:43] Completed 380 of 800 simulation runs
[28-Jun-2021 14:06:50] Completed 381 of 800 simulation runs
[28-Jun-2021 14:06:51] Completed 382 of 800 simulation runs
[28-Jun-2021 14:06:53] Completed 383 of 800 simulation runs
[28-Jun-2021 14:06:54] Completed 384 of 800 simulation runs
[28-Jun-2021 14:06:56] Completed 385 of 800 simulation runs
[28-Jun-2021 14:06:59] Completed 386 of 800 simulation runs
[28-Jun-2021 14:07:00] Completed 387 of 800 simulation runs
[28-Jun-2021 14:07:03] Completed 388 of 800 simulation runs
[28-Jun-2021 14:07:04] Completed 389 of 800 simulation runs
[28-Jun-2021 14:07:07] Completed 390 of 800 simulation runs
[28-Jun-2021 14:07:15] Completed 391 of 800 simulation runs
[28-Jun-2021 14:07:18] Completed 392 of 800 simulation runs
[28-Jun-2021 14:07:19] Completed 393 of 800 simulation runs
[28-Jun-2021 14:07:27] Completed 394 of 800 simulation runs
[28-Jun-2021 14:07:29] Completed 395 of 800 simulation runs
[28-Jun-2021 14:07:31] Completed 396 of 800 simulation runs
[28-Jun-2021 14:07:32] Completed 397 of 800 simulation runs
[28-Jun-2021 14:07:36] Completed 398 of 800 simulation runs
[28-Jun-2021 14:07:38] Completed 399 of 800 simulation runs
[28-Jun-2021 14:07:39] Completed 400 of 800 simulation runs
[28-Jun-2021 14:07:53] Completed 401 of 800 simulation runs
[28-Jun-2021 14:07:55] Completed 402 of 800 simulation runs
[28-Jun-2021 14:07:57] Completed 403 of 800 simulation runs
[28-Jun-2021 14:07:59] Completed 404 of 800 simulation runs
[28-Jun-2021 14:08:01] Completed 405 of 800 simulation runs
[28-Jun-2021 14:08:06] Completed 406 of 800 simulation runs
[28-Jun-2021 14:08:18] Completed 407 of 800 simulation runs
[28-Jun-2021 14:08:20] Completed 408 of 800 simulation runs
[28-Jun-2021 14:08:22] Completed 409 of 800 simulation runs
[28-Jun-2021 14:08:24] Completed 410 of 800 simulation runs
[28-Jun-2021 14:08:27] Completed 411 of 800 simulation runs
[28-Jun-2021 14:08:31] Completed 412 of 800 simulation runs
[28-Jun-2021 14:08:42] Completed 413 of 800 simulation runs
[28-Jun-2021 14:08:44] Completed 414 of 800 simulation runs
[28-Jun-2021 14:08:46] Completed 415 of 800 simulation runs
[28-Jun-2021 14:08:48] Completed 416 of 800 simulation runs
[28-Jun-2021 14:08:52] Completed 417 of 800 simulation runs
[28-Jun-2021 14:08:55] Completed 418 of 800 simulation runs
[28-Jun-2021 14:09:06] Completed 419 of 800 simulation runs
[28-Jun-2021 14:09:09] Completed 420 of 800 simulation runs
[28-Jun-2021 14:09:11] Completed 421 of 800 simulation runs
[28-Jun-2021 14:09:13] Completed 422 of 800 simulation runs
[28-Jun-2021 14:09:15] Completed 423 of 800 simulation runs
[28-Jun-2021 14:09:19] Completed 424 of 800 simulation runs
[28-Jun-2021 14:09:31] Completed 425 of 800 simulation runs
[28-Jun-2021 14:09:33] Completed 426 of 800 simulation runs
[28-Jun-2021 14:09:35] Completed 427 of 800 simulation runs
[28-Jun-2021 14:09:38] Completed 428 of 800 simulation runs
[28-Jun-2021 14:09:40] Completed 429 of 800 simulation runs
[28-Jun-2021 14:09:45] Completed 430 of 800 simulation runs
[28-Jun-2021 14:09:57] Completed 431 of 800 simulation runs
[28-Jun-2021 14:10:00] Completed 432 of 800 simulation runs
[28-Jun-2021 14:10:02] Completed 433 of 800 simulation runs
[28-Jun-2021 14:10:05] Completed 434 of 800 simulation runs
[28-Jun-2021 14:10:07] Completed 435 of 800 simulation runs
[28-Jun-2021 14:10:12] Completed 436 of 800 simulation runs
[28-Jun-2021 14:10:21] Completed 437 of 800 simulation runs
[28-Jun-2021 14:10:25] Completed 438 of 800 simulation runs
[28-Jun-2021 14:10:27] Completed 439 of 800 simulation runs
[28-Jun-2021 14:10:29] Completed 440 of 800 simulation runs
[28-Jun-2021 14:10:31] Completed 441 of 800 simulation runs
[28-Jun-2021 14:10:35] Completed 442 of 800 simulation runs
[28-Jun-2021 14:10:47] Completed 443 of 800 simulation runs
[28-Jun-2021 14:10:51] Completed 444 of 800 simulation runs
[28-Jun-2021 14:10:53] Completed 445 of 800 simulation runs
[28-Jun-2021 14:10:55] Completed 446 of 800 simulation runs
[28-Jun-2021 14:10:57] Completed 447 of 800 simulation runs
[28-Jun-2021 14:10:59] Completed 448 of 800 simulation runs
[28-Jun-2021 14:11:13] Completed 449 of 800 simulation runs
[28-Jun-2021 14:11:15] Completed 450 of 800 simulation runs
[28-Jun-2021 14:11:18] Completed 451 of 800 simulation runs
[28-Jun-2021 14:11:20] Completed 452 of 800 simulation runs
[28-Jun-2021 14:11:22] Completed 453 of 800 simulation runs
[28-Jun-2021 14:11:24] Completed 454 of 800 simulation runs
[28-Jun-2021 14:11:40] Completed 455 of 800 simulation runs
[28-Jun-2021 14:11:42] Completed 456 of 800 simulation runs
[28-Jun-2021 14:11:44] Completed 457 of 800 simulation runs
[28-Jun-2021 14:11:46] Completed 458 of 800 simulation runs
[28-Jun-2021 14:11:48] Completed 459 of 800 simulation runs
[28-Jun-2021 14:11:50] Completed 460 of 800 simulation runs
[28-Jun-2021 14:12:05] Completed 461 of 800 simulation runs
[28-Jun-2021 14:12:06] Completed 462 of 800 simulation runs
[28-Jun-2021 14:12:08] Completed 463 of 800 simulation runs
[28-Jun-2021 14:12:10] Completed 464 of 800 simulation runs
[28-Jun-2021 14:12:12] Completed 465 of 800 simulation runs
[28-Jun-2021 14:12:15] Completed 466 of 800 simulation runs
[28-Jun-2021 14:12:29] Completed 467 of 800 simulation runs
[28-Jun-2021 14:12:31] Completed 468 of 800 simulation runs
[28-Jun-2021 14:12:33] Completed 469 of 800 simulation runs
[28-Jun-2021 14:12:35] Completed 470 of 800 simulation runs
[28-Jun-2021 14:12:37] Completed 471 of 800 simulation runs
[28-Jun-2021 14:12:41] Completed 472 of 800 simulation runs
[28-Jun-2021 14:12:53] Completed 473 of 800 simulation runs
[28-Jun-2021 14:12:55] Completed 474 of 800 simulation runs
[28-Jun-2021 14:12:58] Completed 475 of 800 simulation runs
[28-Jun-2021 14:13:00] Completed 476 of 800 simulation runs
[28-Jun-2021 14:13:02] Completed 477 of 800 simulation runs
[28-Jun-2021 14:13:07] Completed 478 of 800 simulation runs
[28-Jun-2021 14:13:19] Completed 479 of 800 simulation runs
[28-Jun-2021 14:13:21] Completed 480 of 800 simulation runs
[28-Jun-2021 14:13:23] Completed 481 of 800 simulation runs
[28-Jun-2021 14:13:26] Completed 482 of 800 simulation runs
[28-Jun-2021 14:13:28] Completed 483 of 800 simulation runs
[28-Jun-2021 14:13:35] Completed 484 of 800 simulation runs
[28-Jun-2021 14:13:44] Completed 485 of 800 simulation runs
[28-Jun-2021 14:13:46] Completed 486 of 800 simulation runs
[28-Jun-2021 14:13:48] Completed 487 of 800 simulation runs
[28-Jun-2021 14:13:50] Completed 488 of 800 simulation runs
[28-Jun-2021 14:13:52] Completed 489 of 800 simulation runs
[28-Jun-2021 14:14:01] Completed 490 of 800 simulation runs
[28-Jun-2021 14:14:08] Completed 491 of 800 simulation runs
[28-Jun-2021 14:14:10] Completed 492 of 800 simulation runs
[28-Jun-2021 14:14:12] Completed 493 of 800 simulation runs
[28-Jun-2021 14:14:14] Completed 494 of 800 simulation runs
[28-Jun-2021 14:14:16] Completed 495 of 800 simulation runs
[28-Jun-2021 14:14:27] Completed 496 of 800 simulation runs
[28-Jun-2021 14:14:35] Completed 497 of 800 simulation runs
[28-Jun-2021 14:14:37] Completed 498 of 800 simulation runs
[28-Jun-2021 14:14:39] Completed 499 of 800 simulation runs
[28-Jun-2021 14:14:41] Completed 500 of 800 simulation runs
[28-Jun-2021 14:14:43] Completed 501 of 800 simulation runs
[28-Jun-2021 14:14:45] Completed 502 of 800 simulation runs
[28-Jun-2021 14:14:46] Completed 503 of 800 simulation runs
[28-Jun-2021 14:14:50] Completed 504 of 800 simulation runs
[28-Jun-2021 14:14:51] Completed 505 of 800 simulation runs
[28-Jun-2021 14:14:53] Completed 506 of 800 simulation runs
[28-Jun-2021 14:14:54] Completed 507 of 800 simulation runs
[28-Jun-2021 14:14:55] Completed 508 of 800 simulation runs
[28-Jun-2021 14:15:06] Completed 509 of 800 simulation runs
[28-Jun-2021 14:15:08] Completed 510 of 800 simulation runs
[28-Jun-2021 14:15:10] Completed 511 of 800 simulation runs
[28-Jun-2021 14:15:11] Completed 512 of 800 simulation runs
[28-Jun-2021 14:15:13] Completed 513 of 800 simulation runs
[28-Jun-2021 14:15:15] Completed 514 of 800 simulation runs
[28-Jun-2021 14:15:18] Completed 515 of 800 simulation runs
[28-Jun-2021 14:15:23] Completed 516 of 800 simulation runs
[28-Jun-2021 14:15:25] Completed 517 of 800 simulation runs
[28-Jun-2021 14:15:27] Completed 518 of 800 simulation runs
[28-Jun-2021 14:15:29] Completed 519 of 800 simulation runs
[28-Jun-2021 14:15:30] Completed 520 of 800 simulation runs
[28-Jun-2021 14:15:33] Completed 521 of 800 simulation runs
[28-Jun-2021 14:15:35] Completed 522 of 800 simulation runs
[28-Jun-2021 14:15:40] Completed 523 of 800 simulation runs
[28-Jun-2021 14:15:42] Completed 524 of 800 simulation runs
[28-Jun-2021 14:15:46] Completed 525 of 800 simulation runs
[28-Jun-2021 14:15:48] Completed 526 of 800 simulation runs
[28-Jun-2021 14:15:50] Completed 527 of 800 simulation runs
[28-Jun-2021 14:15:51] Completed 528 of 800 simulation runs
[28-Jun-2021 14:15:53] Completed 529 of 800 simulation runs
[28-Jun-2021 14:15:54] Completed 530 of 800 simulation runs
[28-Jun-2021 14:15:57] Completed 531 of 800 simulation runs
[28-Jun-2021 14:16:02] Completed 532 of 800 simulation runs
[28-Jun-2021 14:16:04] Completed 533 of 800 simulation runs
[28-Jun-2021 14:16:05] Completed 534 of 800 simulation runs
[28-Jun-2021 14:16:15] Completed 535 of 800 simulation runs
[28-Jun-2021 14:16:17] Completed 536 of 800 simulation runs
[28-Jun-2021 14:16:19] Completed 537 of 800 simulation runs
[28-Jun-2021 14:16:21] Completed 538 of 800 simulation runs
[28-Jun-2021 14:16:23] Completed 539 of 800 simulation runs
[28-Jun-2021 14:16:25] Completed 540 of 800 simulation runs
[28-Jun-2021 14:16:28] Completed 541 of 800 simulation runs
[28-Jun-2021 14:16:30] Completed 542 of 800 simulation runs
[28-Jun-2021 14:16:39] Completed 543 of 800 simulation runs
[28-Jun-2021 14:16:42] Completed 544 of 800 simulation runs
[28-Jun-2021 14:16:44] Completed 545 of 800 simulation runs
[28-Jun-2021 14:16:48] Completed 546 of 800 simulation runs
[28-Jun-2021 14:16:50] Completed 547 of 800 simulation runs
[28-Jun-2021 14:16:52] Completed 548 of 800 simulation runs
[28-Jun-2021 14:16:55] Completed 549 of 800 simulation runs
[28-Jun-2021 14:16:57] Completed 550 of 800 simulation runs
[28-Jun-2021 14:17:00] Completed 551 of 800 simulation runs
[28-Jun-2021 14:17:06] Completed 552 of 800 simulation runs
[28-Jun-2021 14:17:12] Completed 553 of 800 simulation runs
[28-Jun-2021 14:17:14] Completed 554 of 800 simulation runs
[28-Jun-2021 14:17:16] Completed 555 of 800 simulation runs
[28-Jun-2021 14:17:18] Completed 556 of 800 simulation runs
[28-Jun-2021 14:17:20] Completed 557 of 800 simulation runs
[28-Jun-2021 14:17:23] Completed 558 of 800 simulation runs
[28-Jun-2021 14:17:25] Completed 559 of 800 simulation runs
[28-Jun-2021 14:17:28] Completed 560 of 800 simulation runs
[28-Jun-2021 14:17:31] Completed 561 of 800 simulation runs
[28-Jun-2021 14:17:34] Completed 562 of 800 simulation runs
[28-Jun-2021 14:17:36] Completed 563 of 800 simulation runs
[28-Jun-2021 14:17:37] Completed 564 of 800 simulation runs
[28-Jun-2021 14:17:42] Completed 565 of 800 simulation runs
[28-Jun-2021 14:17:46] Completed 566 of 800 simulation runs
[28-Jun-2021 14:17:48] Completed 567 of 800 simulation runs
[28-Jun-2021 14:17:50] Completed 568 of 800 simulation runs
[28-Jun-2021 14:17:52] Completed 569 of 800 simulation runs
[28-Jun-2021 14:17:57] Completed 570 of 800 simulation runs
[28-Jun-2021 14:17:59] Completed 571 of 800 simulation runs
[28-Jun-2021 14:18:07] Completed 572 of 800 simulation runs
[28-Jun-2021 14:18:09] Completed 573 of 800 simulation runs
[28-Jun-2021 14:18:11] Completed 574 of 800 simulation runs
[28-Jun-2021 14:18:13] Completed 575 of 800 simulation runs
[28-Jun-2021 14:18:15] Completed 576 of 800 simulation runs
[28-Jun-2021 14:18:17] Completed 577 of 800 simulation runs
[28-Jun-2021 14:18:30] Completed 578 of 800 simulation runs
[28-Jun-2021 14:18:32] Completed 579 of 800 simulation runs
[28-Jun-2021 14:18:34] Completed 580 of 800 simulation runs
[28-Jun-2021 14:18:35] Completed 581 of 800 simulation runs
[28-Jun-2021 14:18:38] Completed 582 of 800 simulation runs
[28-Jun-2021 14:18:40] Completed 583 of 800 simulation runs
[28-Jun-2021 14:18:42] Completed 584 of 800 simulation runs
[28-Jun-2021 14:18:44] Completed 585 of 800 simulation runs
[28-Jun-2021 14:18:45] Completed 586 of 800 simulation runs
[28-Jun-2021 14:18:47] Completed 587 of 800 simulation runs
[28-Jun-2021 14:18:49] Completed 588 of 800 simulation runs
[28-Jun-2021 14:18:56] Completed 589 of 800 simulation runs
[28-Jun-2021 14:18:58] Completed 590 of 800 simulation runs
[28-Jun-2021 14:19:00] Completed 591 of 800 simulation runs
[28-Jun-2021 14:19:03] Completed 592 of 800 simulation runs
[28-Jun-2021 14:19:05] Completed 593 of 800 simulation runs
[28-Jun-2021 14:19:07] Completed 594 of 800 simulation runs
[28-Jun-2021 14:19:12] Completed 595 of 800 simulation runs
[28-Jun-2021 14:19:14] Completed 596 of 800 simulation runs
[28-Jun-2021 14:19:16] Completed 597 of 800 simulation runs
[28-Jun-2021 14:19:18] Completed 598 of 800 simulation runs
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ens = simulationEnsembleDatastore(fullfile('.','Data'));

Обработка и извлечение функций от результатов симуляции

Модель сконфигурирована, чтобы регистрировать давление выхода насоса, вывести поток, частоту вращения двигателя и моторный ток. 

ens.DataVariables
ans = 8×1 string
    "SimulationInput"
    "SimulationMetadata"
    "iMotor_meas"
    "pIn_meas"
    "pOut_meas"
    "qIn_meas"
    "qOut_meas"
    "wMotor_meas"

Поскольку каждый член в ансамбле предварительно обрабатывает выход насоса, текут и вычисляют индикаторы состояния на основе потока выхода насоса. Индикаторы состояния позже используются для классификации отказов. Поскольку предварительная обработка удаляет первые 0,8 секунды выходного потока, когда это содержит переходные процессы от запуска насоса и симуляции. Как часть предварительной обработки вычисляют спектр мощности выходного потока и используют SimulationInput, чтобы возвратить значения переменных отказа.

Сконфигурируйте ансамбль так, чтобы возвраты только для чтения переменные интереса и вызвали preprocess функция, которая задана в конце этого примера.

ens.SelectedVariables = ["qOut_meas", "SimulationInput"];
reset(ens)
data = read(ens)
data=1×2 table
        qOut_meas                SimulationInput        
    __________________    ______________________________

    {2001×1 timetable}    {1×1 Simulink.SimulationInput}


[flow,flowP,flowF,faultValues] = preprocess(data);

Постройте спектр потока и поток. Отображенные на графике данные являются для отказа свободным условием.

% Figure with nominal
subplot(211);
plot(flow.Time,flow.Data);
subplot(212);
semilogx(flowF,pow2db(flowP));
xlabel('Hz')

Спектр потока показывает резонирующий peaks на ожидаемых частотах. А именно, скорость электродвигателя насоса составляет 950 об/мин или 15,833 Гц, и поскольку насос имеет 3 цилиндра, поток, как ожидают, будет иметь основной принцип на уровне 3*15.833 Гц или 47,5 Гц, а также гармоники во множителях 47,5 Гц. Спектр потока ясно показывает ожидаемый резонирующий peaks. Отказы в одном цилиндре насоса приведут к резонансам на скорости электродвигателя насоса, 15,833 Гц и ее гармониках.

Спектр потока и медленный сигнал дают некоторое представление о возможных индикаторах состояния. А именно, общая статистика сигнала, такая как среднее значение, отклонение, и т.д. а также характеристики спектра. Вычисляются индикаторы состояния спектра, относящиеся к ожидаемым гармоникам, таким как частота с пиковой величиной, энергия приблизительно 15,833 Гц, энергия приблизительно 47,5 Гц, энергия выше 100 Гц. Частота спектрального пика эксцесса также вычисляется.

Сконфигурируйте ансамбль с переменными данных для индикаторов состояния и условными переменными для значений переменных отказа. Затем вызовите extractCI функция, чтобы вычислить функции и использовать writeToLastMemberRead команда, чтобы добавить опцию и дать сбой значения переменных ансамблю. extractCI функция задана в конце этого примера. 

ens.DataVariables = [ens.DataVariables; ...
    "fPeak"; "pLow"; "pMid"; "pHigh"; "pKurtosis"; ...
    "qMean"; "qVar"; "qSkewness"; "qKurtosis"; ...
    "qPeak2Peak"; "qCrest"; "qRMS"; "qMAD"; "qCSRange"];
ens.ConditionVariables = ["LeakFault","BlockingFault","BearingFault"];

feat = extractCI(flow,flowP,flowF);
dataToWrite = [faultValues, feat];
writeToLastMemberRead(ens,dataToWrite{:})

Вышеупомянутый код предварительно обрабатывает и вычисляет индикаторы состояния для первого члена ансамбля симуляции. Повторите это для всех членов в ансамбле, использующем ансамбль hasdata команда. Чтобы понять результаты симуляции при различных условиях отказа строят каждый сотый элемент ансамбля.

%Figure with nominal and faults
figure,
subplot(211);
lFlow = plot(flow.Time,flow.Data,'LineWidth',2);
subplot(212);
lFlowP = semilogx(flowF,pow2db(flowP),'LineWidth',2);
xlabel('Hz')
ct = 1;
lColors = get(lFlow.Parent,'ColorOrder');
lIdx = 2;

% Loop over all members in the ensemble, preprocess 
% and compute the features for each member
while hasdata(ens)
    
    % Read member data
    data = read(ens);
    
    % Preprocess and extract features from the member data
    [flow,flowP,flowF,faultValues] = preprocess(data);
    feat = extractCI(flow,flowP,flowF);
    
    % Add the extracted feature values to the member data
    dataToWrite = [faultValues, feat];
    writeToLastMemberRead(ens,dataToWrite{:})
    
    % Plot member signal and spectrum for every 100th member
    if mod(ct,100) == 0
        line('Parent',lFlow.Parent,'XData',flow.Time,'YData',flow.Data, ...
            'Color', lColors(lIdx,:));
        line('Parent',lFlowP.Parent,'XData',flowF,'YData',pow2db(flowP), ...
            'Color', lColors(lIdx,:));
        if lIdx == size(lColors,1)
            lIdx = 1;
        else
            lIdx = lIdx+1;
        end
    end
    ct = ct + 1;
end

Обратите внимание на то, что при различных условиях отказа и строгом обращении спектр содержит гармоники на ожидаемых частотах.

Обнаружьте и классифицируйте отказы насоса

Предварительно обработанные и вычисленные индикаторы состояния предыдущего раздела от потока сигнализируют для всех членов ансамбля симуляции, которые соответствуют результатам симуляции для различных комбинаций отказа и строгого обращения. Индикаторы состояния могут использоваться, чтобы обнаружить и классифицировать отказы насоса от сигнала потока насоса.

Сконфигурируйте ансамбль симуляции, чтобы считать индикаторы состояния, и использовать высокое и собрать команды, чтобы загрузить все индикаторы состояния и дать сбой значения переменных в память

% Get data to design a classifier.
reset(ens)
ens.SelectedVariables = [...
    "fPeak","pLow","pMid","pHigh","pKurtosis",...
    "qMean","qVar","qSkewness","qKurtosis",...
    "qPeak2Peak","qCrest","qRMS","qMAD","qCSRange",...
    "LeakFault","BlockingFault","BearingFault"];
idxLastFeature = 14;

% Load the condition indicator data into memory
data = gather(tall(ens));
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: 0% complete
Evaluation 0% complete

- Pass 1 of 1: Completed in 37 sec
Evaluation completed in 37 sec

data(1:10,:)
ans=10×17 table
    fPeak      pLow       pMid     pHigh     pKurtosis    qMean      qVar     qSkewness    qKurtosis    qPeak2Peak    qCrest     qRMS      qMAD     qCSRange    LeakFault    BlockingFault    BearingFault
    ______    _______    ______    ______    _________    ______    ______    _________    _________    __________    ______    ______    ______    ________    _________    _____________    ____________

    43.909     0.8468    117.73    18.855     276.49      35.573    7.5235    -0.73064       2.778         13.86      1.1491    35.679    2.2319     42692         1e-09          0.8              0      
    43.909     0.4622    125.98    18.956     12.417      35.577    7.8766    -0.70939        2.63        13.336      1.1451    35.688    2.3235     42699         1e-09          0.8              0      
    43.909     1.1679    138.01     17.54     11.589      35.575    7.4403     -0.7229      2.7135         12.61      1.1394    35.679    2.2415     42696         1e-09          0.8              0      
    14.779     235.27    193.49    26.728     197.02      33.223    15.242    -0.24387      2.2772         18.69       1.232    33.451     3.256     39862       3.2e-06          0.8              0      
    14.779     287.41    198.79    25.321     487.58      32.955    17.606    -0.20213        2.22        19.064      1.2304    33.221    3.5482     39539       3.6e-06          0.8              0      
    43.848     4.3805    137.31    19.175     110.93      35.275    7.5471    -0.70987      2.7751        13.858      1.1586    35.382    2.2525     42335         4e-07          0.8              0      
    14.839     303.74    176.33    23.665     392.38      32.908    17.638    -0.19533       2.237        19.561      1.2376    33.175    3.5358     39484       3.6e-06          0.8              0      
    44.151     133.99    159.09    26.973      434.6       33.76    12.137    -0.37195      2.5289        16.849      1.1925    33.939    2.8477     40507       2.4e-06          0.8              0      
    43.848    0.43902    134.89    18.997     12.417      35.562    7.8636     -0.6853      2.6661         13.91      1.1502    35.673    2.3088     42682         1e-09          0.8              0      
    43.969     12.292    124.71    22.448     478.48      34.986    8.0954    -0.71245      2.9268        15.015      1.1585    35.101    2.2984     41987         8e-07          0.8              0

Значения переменных отказа для каждого члена ансамбля (строка в таблице данных) могут быть преобразованы во флаги отказа и флаги отказа, объединенные к одному флагу, который получает различное состояние отказа каждого члена.

% Convert the fault variable values to flags
data.LeakFlag = data.LeakFault > 1e-6;
data.BlockingFlag = data.BlockingFault < 0.8;
data.BearingFlag = data.BearingFault > 0; 
data.CombinedFlag = data.LeakFlag+2*data.BlockingFlag+4*data.BearingFlag;

Создайте классификатор, который берет в качестве входа индикаторы состояния и возвращает объединенный флаг отказа. Обучите машину опорных векторов, которая использует 2-е ядро полинома порядка. Используйте cvpartition команда, чтобы разделить члены ансамбля в набор для обучения и набор для валидации.

rng('default') % for reproducibility
predictors = data(:,1:idxLastFeature); 
response = data.CombinedFlag;
cvp = cvpartition(size(predictors,1),'KFold',5);

% Create and train the classifier
template = templateSVM(...
    'KernelFunction', 'polynomial', ...
    'PolynomialOrder', 2, ...
    'KernelScale', 'auto', ...
    'BoxConstraint', 1, ...
    'Standardize', true);
combinedClassifier = fitcecoc(...
    predictors(cvp.training(1),:), ...
    response(cvp.training(1),:), ...
    'Learners', template, ...
    'Coding', 'onevsone', ...
    'ClassNames', [0; 1; 2; 3; 4; 5; 6; 7]);

Проверяйте эффективность обученного классификатора с помощью данных о валидации и постройте результаты на графике беспорядка.

% Check performance by computing and plotting the confusion matrix
actualValue = response(cvp.test(1),:);
predictedValue = predict(combinedClassifier, predictors(cvp.test(1),:));
confdata = confusionmat(actualValue,predictedValue);
figure,
labels = {'None', 'Leak','Blocking', 'Leak & Blocking', 'Bearing', ...
    'Bearing & Leak', 'Bearing & Blocking', 'All'};
h = heatmap(confdata, ...
    'YLabel', 'Actual leak fault', ...
    'YDisplayLabels', labels, ...
    'XLabel', 'Predicted fault', ...
    'XDisplayLabels', labels, ...
    'ColorbarVisible','off');

График беспорядка показывает для каждой комбинации отказов число раз, комбинация отказа была правильно предсказана (диагональные элементы графика) и число раз, комбинация отказа была неправильно предсказана (недиагональные записи).

График беспорядка показывает, что классификатор правильно не классифицировал некоторые условия отказа (от диагональных терминов). Однако никакое условие отказа не было правильно предсказано. В нескольких местах не было предсказано никакое условие отказа, когда был отказ (первый столбец), в противном случае отказ был предсказан несмотря на то, что это не может быть точно правильное условие отказа. В целом точность валидации составляла 66% и точность при предсказании, что существует отказ 94%.

% Compute the overall accuracy of the classifier
sum(diag(confdata))/sum(confdata(:))
ans = 0.6625

% Compute the accuracy of the classifier at predicting 
% that there is a fault
1-sum(confdata(2:end,1))/sum(confdata(:))
ans = 0.9375

Исследуйте случаи, где никакой отказ не был предсказан, но отказ действительно существовал. Сначала найдите случаи в данных о валидации, где фактический отказ был блокирующимся отказом, но никакой отказ не был предсказан.

vData = data(cvp.test(1),:);
b1 = (actualValue==2) & (predictedValue==0);
fData = vData(b1,15:17)
fData=10×3 table
    LeakFault    BlockingFault    BearingFault
    _________    _____________    ____________

      1e-09          0.71              0      
      1e-09          0.77              0      
      1e-09          0.71              0      
      1e-09          0.77              0      
      1e-09          0.71              0      
      1e-09          0.77              0      
      1e-09          0.71              0      
      1e-09          0.74              0      
      1e-09          0.77              0      
      1e-09          0.77              0

Найдите случаи в данных о валидации, где фактический отказ был отказом подшипника, но никакой отказ не был предсказан.

b2 = (actualValue==4) & (predictedValue==0);
vData(b2,15:17)
ans =

  0×3 empty table

При исследовании случаев, где никакой отказ не был прогнозирующим, но действительно существовал отказ, показывает, что они происходят, когда блокирующееся значение отказа 0,77 близко к его номинальной стоимости 0,8, или значение отказа подшипника 6.6e-5 близко к его номинальной стоимости 0. Графический вывод спектра для случая с маленьким значением отказа блокирования и сравнения с отказом, свободное условие показывает, что спектры являются очень похожим трудным обнаружением создания. Переобучение классификатора, но включая блокирующееся значение 0,77 как не дает сбой, условие значительно улучшало бы производительность детектора отказа. В качестве альтернативы использование дополнительных измерений насоса могло предоставить больше информации и улучшить способность обнаружить маленькие отказы блокирования.

% Configure the ensemble to only read the flow and fault variable values
ens.SelectedVariables = ["qOut_meas","LeakFault","BlockingFault","BearingFault"];
reset(ens)

% Load the ensemble member data into memory
data = gather(tall(ens));
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: 0% complete
Evaluation 0% complete

- Pass 1 of 1: Completed in 31 sec
Evaluation completed in 31 sec

% Look for the member that was incorrectly predicted, and 
% compute its power spectrum
idx = ...
    data.BlockingFault == fData.BlockingFault(1) & ...
    data.LeakFault == fData.LeakFault(1) & ...
    data.BearingFault == fData.BearingFault(1);
flow1 = data(idx,1);
flow1 = flow1.qOut_meas{1};
[flow1P,flow1F] = pspectrum(flow1);

% Look for a member that does not have any faults
idx = ...
    data.BlockingFault == 0.8 & ...
    data.LeakFault == 1e-9 & ...
    data.BearingFault == 0;
flow2 = data(idx,1);
flow2 = flow2.qOut_meas{1};
[flow2P,flow2F] = pspectrum(flow2);

% Plot the power spectra
semilogx(...
    flow1F,pow2db(flow1P),...
    flow2F,pow2db(flow2P));
xlabel('Hz')
legend('Small blocking fault','No fault')

Заключение

Этот пример показал, как использовать модель Simulink для отказов модели в поршневом насосе, симулировать модель под различными комбинациями отказа и строгим обращением, индикаторы состояния извлечения от выхода насоса текут и используют индикаторы состояния, чтобы обучить классификатор обнаруживать отказы насоса. Пример исследовал эффективность обнаружения отказа с помощью классификатора и отметил, что маленькие отказы блокирования не очень похожи ни на какое условие отказа и не могут быть надежно обнаружены.

Вспомогательные Функции

function [flow,flowSpectrum,flowFrequencies,faultValues] = preprocess(data)
% Helper function to preprocess the logged reciprocating pump data.

% Remove the 1st 0.8 seconds of the flow signal
tMin = seconds(0.8);
flow = data.qOut_meas{1};
flow = flow(flow.Time >= tMin,:);
flow.Time = flow.Time - flow.Time(1);

% Ensure the flow is sampled at a uniform sample rate
flow = retime(flow,'regular','linear','TimeStep',seconds(1e-3));

% Remove the mean from the flow and compute the flow spectrum
fA = flow;
fA.Data = fA.Data - mean(fA.Data);
[flowSpectrum,flowFrequencies] = pspectrum(fA,'FrequencyLimits',[2 250]);

% Find the values of the fault variables from the SimulationInput
simin = data.SimulationInput{1};
vars = {simin.Variables.Name};
idx = strcmp(vars,'leak_cyl_area_WKSP');
LeakFault = simin.Variables(idx).Value;
idx = strcmp(vars,'block_in_factor_WKSP');
BlockingFault = simin.Variables(idx).Value;
idx = strcmp(vars,'bearing_fault_frict_WKSP');
BearingFault = simin.Variables(idx).Value;

% Collect the fault values in a cell array
faultValues = {...
    'LeakFault', LeakFault, ...
    'BlockingFault', BlockingFault, ...
    'BearingFault', BearingFault};
end

function ci = extractCI(flow,flowP,flowF)
% Helper function to extract condition indicators from the flow signal 
% and spectrum.

% Find the frequency of the peak magnitude in the power spectrum.
pMax = max(flowP);
fPeak = flowF(flowP==pMax);

% Compute the power in the low frequency range 10-20 Hz.
fRange = flowF >= 10 & flowF <= 20;
pLow = sum(flowP(fRange));

% Compute the power in the mid frequency range 40-60 Hz.
fRange = flowF >= 40 & flowF <= 60;
pMid = sum(flowP(fRange));

% Compute the power in the high frequency range >100 Hz.
fRange = flowF >= 100;
pHigh = sum(flowP(fRange));

% Find the frequency of the spectral kurtosis peak
[pKur,fKur] = pkurtosis(flow);
pKur = fKur(pKur == max(pKur));

% Compute the flow cumulative sum range.
csFlow = cumsum(flow.Data);
csFlowRange = max(csFlow)-min(csFlow);

% Collect the feature and feature values in a cell array. 
% Add flow statistic (mean, variance, etc.) and common signal 
% characteristics (rms, peak2peak, etc.) to the cell array.
ci = {...
    'qMean', mean(flow.Data), ...
    'qVar',  var(flow.Data), ...
    'qSkewness', skewness(flow.Data), ...
    'qKurtosis', kurtosis(flow.Data), ...
    'qPeak2Peak', peak2peak(flow.Data), ...
    'qCrest', peak2rms(flow.Data), ...
    'qRMS', rms(flow.Data), ...
    'qMAD', mad(flow.Data), ...
    'qCSRange',csFlowRange, ...
    'fPeak', fPeak, ...
    'pLow', pLow, ...
    'pMid', pMid, ...
    'pHigh', pHigh, ...
    'pKurtosis', pKur(1)};
end

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