Multidomain Features-Based GA Optimized Artificial Immune System for Bearing Fault Detection

This paper proposes a novel multidomain features-based genetic algorithm (GA) optimized artificial immune system (AIS) framework for fault detection in real systems. Different from native real-valued negative selection algorithm (RNSA) that operates in original data space, this algorithm utilizes fe...

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Bibliographic Details
Published in:IEEE transactions on systems, man, and cybernetics. Systems Vol. 50; no. 1; pp. 348 - 359
Main Authors: Abid, Anam, Khan, Muhammad Tahir, Khan, Muhammad Salman
Format: Journal Article
Language:English
Published: New York IEEE 01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Anomaly detection
Artificial immune system (AIS)
Artificial intelligence
Clustering
deep learning
Detectors
Fault detection
Fault diagnosis
Feature extraction
feature selection
genetic algorithm (GA)
Genetic algorithms
Immune system
Machine learning
negative selection algorithm (NSA)
Neural networks
Signal processing
Training
Transformations
ISSN:2168-2216, 2168-2232
Online Access:Get full text
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Summary:This paper proposes a novel multidomain features-based genetic algorithm (GA) optimized artificial immune system (AIS) framework for fault detection in real systems. Different from native real-valued negative selection algorithm (RNSA) that operates in original data space, this algorithm utilizes feature space transformation and diversity factor-based GA for optimized detector distribution in nonself feature space. The proposed framework comprises three stages namely; feature extraction, unsupervised feature selection, and GA optimized AIS. In the first stage, signal processing methods are applied to extract multidomain features (time-domain statistical, frequency domain statistical, and special features) of the system. In the second stage, two unsupervised methods namely, k-NN clustering and pretraining using deep learning neural network are proposed for dominant fault-characterizing feature selection. Finally, in the third stage, the fault-characterizing feature vectors are used for system status categorization (i.e., normal, fault) using selected (fault-characterizing) features-based AIS method. The efficacy of the proposed framework is verified through experiments on motor bearing fault detection using vibration signal. The major accomplishment of the proposed combination of space transformation, feature selection and AIS (anomaly classification) techniques is the alleviation of computational burden on RNSA implementation. Moreover, GA optimized AIS fault diagnosis based on well-established features gives improved detection performance.
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ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2017.2746762