Abstract
Detection and severity identification of mechanical and electrical faults by means of noninvasive methods such as electrical signatures of induction machine have attracted much attention in recent years. Since operating conditions of machines and severity of faults in incipient stages influence the amplitude of fault index in the fault detection process, diagnosing fault occurrence and severity can be more complicated. In this study, an efficient method for fault detection and classification in induction machine based on deep neural networks is introduced. The introduced method applies the long short-term memory (LSTM) and fully convolutional neural networks (FCNs) in a conjoined manner. The authors use the FCN architecture for feature extraction from the time-series signal and augment it with LSTM to improve classification performance. This structure has not been previously applied for fault severity detection in induction machine systems. The authors avoid manual feature engineering and, by eliminating the preprocessing phase, directly use time series of electrical signals for fault detection and classifications. The experimental results have been carried out in different fault severities and loads. The analysis of the results and comparison with other deep and classical methods show that the faulty cases can be separated based on severity and load levels with a high accuracy (98.92%), which shows that the adopted architecture is successful in automatically extracting discriminative features from the signal.
Highlights
Detection and severity identification of mechanical and electrical faults by means of noninvasive methods such as electrical signatures of induction machine have attracted much attention in recent years
To assess the performance of the detection model, the authors trained the two long short-term memory (LSTM)-fully convolutional neural networks (FCNs) and ALSTMFCN using the data of engine 1 and computed the evaluation metrics per class, based on the test data from machine 2. e precision and recall of LSTM-FCN and ALSTM-FCN are presented in Tables 2 and 3, respectively
The precision, recall, and F1 scores are shown for the healthy and fault categories averaged over different slip values. Both networks are competing in detecting the healthy cases according to different metrics. e ALSTMFCN is superior or competing in the faulty cases from the precision perspective, meaning that it has relatively higher true positives and/or less false positives in these classes
Summary
E effects of UWF in the rotor windings of WRIM in the frequency domain of stator current are named as fault index (fi) and can be observed as additive frequencies around the supply frequency (fs) (equation (1), Figure 2(a) and 2(b)) [1]. E prime mover is responsible to rotate WRIG at different rotational speeds In this regard, in different load levels and fault severities in healthy and faulty conditions, stator current signature has been observed. Each class can be identified based on fault severity (resistance unbalance (Runb)) and load level (s: slip). It is necessary to note that the fault characteristic frequency in the case of healthy condition cannot be detected clearly in the spectrum of stator current and the amplitude modulation can be found out in the stator current of machine (Figures 2(e) and 2(f )). Erefore, 22500 × 45 data are considered for the training process and the same number of data is used for the testing process
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