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Tandem connectionist anomaly detection: Use of faulty vibration signals in feature representation learning

This document proposes a method called tandem connectionist anomaly detection that uses faulty vibration data from non-target machines to improve anomaly detection performance on a target machine. The method uses a deep neural network trained on both normal and faulty non-target data to learn discriminative features, which are then used as input to a Gaussian mixture model anomaly detector trained on normal target data. Experiments show this method significantly improves anomaly detection compared to using hand-crafted features or transferring just the detector. It demonstrates the ability to transfer the system between machines of both the same type and different types.

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Related topics:
Deep Learning•Transfer Learning Uses• Wind Turbine•
Tandem Connectionist Anomaly Detection:  Use of Faulty Vibration Signals in Feature  Representation Learning 1 Takanori Hasegawa Jun Ogata Tetsuji  Ogawa Masahiro Murakawa This work was partly supported by the New Energy and Industrial  Technology Development Organization (NEDO).
2https://www.nrel.gov/continuum/partnering/wind.html Unexpected breakdowns can  inflict enormous damage on  society To avoid dangerous events,  regular monitoring and  maintenance are carried out
3https://www.nrel.gov/continuum/partnering/wind.html In data‐driven approach, we  need to collect various types of  data and build anomaly detector Many costs in operation
4https://www.nrel.gov/continuum/partnering/wind.html Effective use of faulty‐state data for  robust and accurate anomaly  detection by transferring existing  system
Typical Data‐Driven Anomaly Detection 5 • Anomaly detection systems are developed  using only normal data obtained from monitoring target machine. • Hand‐crafted features have been designed  to distinguish faulty data from normal data. • Unexpected harmful faulty state pattern  can increase errors. Machine A (Monitoring Target) normal data anomaly data Anomaly detector
Use of Data From Damaged Target Machine 6 Faulty states are rarely observed on  target machine. Detection performance is improved  because property of anomaly (faulty)  state of target machine becomes known. Machine A (Monitoring Target) normal data anomaly data Anomaly Detector
Effective Use of Existing Faulty‐Data 7 normal data Machine A (target) normal data faulty data Feature Extractor System Transfer Machine B (non‐target) Normal and faulty data collected from non‐ target machines are used to learn feature  representations that contribute to  discrimination of normal and faulty data. Anomaly Detector
Proposed  Method 8
9 training normal state  model feature  extraction scoring thresholding Development of anomaly detector OperationDevelopment of  feature extractor training feature  extractor feature  extraction
10 Development of  feature extractor training feature  extractor
11 training normal state  model Development of anomaly detector Development of  feature extractor training feature  extractor feature  extraction
12 training normal state  model Development of anomaly detector Development of  feature extractor training feature  extractor feature  extraction
13 training normal state  model feature  extraction Development of anomaly detector OperationDevelopment of  feature extractor training feature  extractor feature  extraction
14 training normal state  model feature  extraction scoring Development of anomaly detector OperationDevelopment of  feature extractor training feature  extractor feature  extraction
15 training normal state  model feature  extraction scoring thresholding Development of anomaly detector OperationDevelopment of  feature extractor training feature  extractor feature  extraction
16 training normal state  model feature  extraction scoring thresholding Development of anomaly detector OperationDevelopment of  feature extractor training feature  extractor feature  extraction This system can run  even if machine type are  different to each other.
Pre‐processing for Vibration Signals Spectrogram 17 STFT Fourier transform is applied to  vibration signal for short‐time interval  using sliding window.  (e.g. 5 sec for low‐speed machines)
Spectrogram 18 STFT Pre‐processing for Vibration Signals Fourier transform is applied to  vibration signal for short‐time interval  using sliding window.  (e.g. 5 sec for low‐speed machines)
Spectrogram 19 STFT Pre‐processing for Vibration Signals Fourier transform is applied to  vibration signal for short‐time interval  using sliding window.  (e.g. 5 sec for low‐speed machines)
Spectrogram 20 STFT Pre‐processing for Vibration Signals Fourier transform is applied to  vibration signal for short‐time interval  using sliding window.  (e.g. 5 sec for low‐speed machines)
Spectrogram 21 STFT Pre‐processing for Vibration Signals Fourier transform is applied to  vibration signal for short‐time interval  using sliding window.  (e.g. 5 sec for low‐speed machines)
Spectrogram 22 Pre‐processing for Vibration Signals
Spectrogram 23 Mel Filter Pre‐processing for Vibration Signals 15‐dim time
Discriminative Feature Extraction 24 32 units 32 units 32 units units 32 units 1 15 units normal/faulty (labels) non‐target normal, faulty 15‐dim filter‐bank outputs (vibration signals) 15‐dim time
Discriminative Feature Extraction 25 32 units 32 units 32 units units 32 units 1 15 units normal/faulty (labels) non‐target normal, faulty Verifier (decoder) Feature extractor (encoder) 15‐dim filter‐bank outputs (vibration signals) Bottle‐neck feature (e.g n=8 in our experiment)
Discriminative Feature Extraction 26 Verifier (decoder) Feature extractor (encoder) Bottle‐neck feature • Low‐dimensional features  essential for normal/faulty  discrimination • It is expected to remove  redundant features such as  machine‐specific  characteristics and operating  conditions. 32 units 32 units 32 units units 32 units 1 15 units normal/faulty (labels) non‐target normal, faulty 15‐dim filter‐bank outputs (vibration signals)
DNN/GMM Tandem Connectionist Anomaly Detection 27 32 units 32 units 32 units units 32 units 1 15 units Gaussian  mixture model units Bottle‐neck feature • Anomaly detector is trained  on normal data from target  machine. • Feature extractor is trained  on normal/faulty data from  non‐target machine. Use of DNN‐based normal/faulty classifier as feature extractor target normal Verifier (decoder) Feature extractor (encoder) 15‐dim filter‐bank outputs (vibration signals)
28
29 Experiment Non‐ target target 1 Type‐dependent: same type but mounted on  different machines main  bearing main  bearing 2 Type‐independent: different type and mounted on  different machines gearbox main  bearing Experiments on Anomaly Detection of Wind Turbines
Vibration Materials A.07 B.21 NREL Target Machine Main bearing Main bearing Gearbox Ring gear Label Healthy, Faulty Healthy, Faulty Healthy, Faulty Faulty type Exfoliation  damage on  raceway surfaces Exfoliation damage  on both rolling  element and  raceway surfaces Scuffing and polishing Seconds per  record 40 sec 40 sec 60 sec Purpose Testing Ex1: Feature  extractor training Ex2: Feature  extractor training 30 • Recorded at NEDO Smart Maintenance PJ • Located different sites • Different specification of main bearing A.07, B.21
31 Systems Features Verifiers FLAC‐tGMM FLAC (hand crafted) ※ effective in vibration‐ based anomaly detection [WWEC+16 Ogata, et al] target GMM ntDNN non‐target DNN/BNF non‐target MLP ntDNN/BNF‐tGMM non‐target DNN/BNF target GMM
32 Systems Features Verifiers FLAC‐tGMM FLAC (hand crafted) ※ effective in vibration‐ based anomaly detection [WWEC+16 Ogata, et al] target GMM ntDNN/BNF‐tGMM non‐target DNN/BNF target GMM target normal target normal data non‐target normal data faulty data
33 Systems Features Verifiers FLAC‐tGMM FLAC (hand crafted) ※振動異常検知におい て⾼性能 [WWEC+16 Ogata, et al] target GMM ntDNN non‐target DNN/BNF non‐target MLP ntDNN/BNF‐tGMM non‐target DNN/BNF target GMM target normal data non‐target normal data faulty data non‐target normal data faulty data non‐target normal data faulty data
34 Experiment transfer source transfer destination (target) 1 Type‐dependent: same type but mounted on  different machines main  bearing main  bearing 2 Type‐independent: different type and mounted on  different machines gearbox main  bearing
Target: Main‐bearing A.07 Training of Feature Extractor: Main‐bearing B.21 35 Transferring feature  extractor & detector  (DNN) Transferring feature  extractor (DNN/GMM‐tandem) Discriminative feature (BNF) improves  detection performance a lot.
Target: Main‐bearing A.07 Training of Feature Extractor: Main‐bearing B.21 36 Best method will yield a point  in the upper left corner Proportion of faulty  samples that are  correctly identified  as anomaly Proportion of normal  samples that are  incorrectly identified  as anomaly 
Target: Main‐bearing A.07 Training of Feature Extractor: Main‐bearing B.21 37 Transferring feature  extractor & detector  (DNN) Transferring feature  extractor (DNN/GMM‐tandem) Discriminative feature (BNF) improves  detection performance a lot.
Target: Main‐bearing A.07 Training of Feature Extractor: Gearbox 38 DNN/GMM tandem system  achieves significant improvement Transferring feature  extractor & detector  (DNN) Transferring feature  extractor (DNN/GMM‐tandem)
39 Anomaly score trend for Main‐bearing A.07 Training of Feature Extractor: Main‐bearing B.21Anomalyscore Main bearing replacement Training GMM
Take‐home Messages 40 Aim: Effective use of faulty data observed from non‐target Proposal: DNN/GMM tandem connectionist anomaly detection Result: Significant improvement in detection performance Experiments transfer of  FE + AD (DNN) transfer of FE (DNN/GMM) 1 Capability of transferring  system among machines  with same types 2 Capability of transferring  system among machines  with different types