# An Audio-Based Motor-Fault Diagnosis System with SOM-LSTM
###### Abstract
[16] employed frequency-axis proportional correction, load segmentation, and feature extraction methods to process the motor sound and used the generalized regression neural network to train and identify induction motor faults. Ref. [17] applied the artificial intelligence algorithm to solve the problem of fault diagnosis in a permanent magnet synchronous machine. Refs. [18; 19] proposed a deep transfer learning strategy in the intelligent fault diagnosis of rotating machinery. Ref. [20] investigated a novel diagnosis algorithm for incipient electrical faults in an induction machine of wind power systems under time-varying conditions. Ref. [21] used the motor current normalized residual harmonic analysis method to detect the diagnosis of induction motor faults. Multilayer artificial neural networks (MANNs) have been used for the diagnosis and classification of different faults in the stator windings of a permanent magnet synchronous motor [22]. In order to solve the problem of the compound fault diagnosis of diesel engine fuel, a diagnosis method based on generative adversarial networks and transfer learning was proposed in [23]. According to a previous literature review, the detection of motor faults is much more based on their severity and location. In addition, when different loads are applied to the motor operation, the sound signals are also different, which causes trouble in fault detection. The challenge of this work is to detect and estimate the severity of motor faults under different loads to detect the various types of motor faults. This study combined self-organizing maps (SOMs) [24; 25] with long short-term memory (LSTM) [26; 27] to build a sound-based motor-fault diagnosis system in order to solve the problem of motor-abnormality detection. The spectrogram data of FFT may be highly complex and multi-dimensional, and the correlation thereof may still be very complex. SOMs, which are thus used for data cluster analysis, are unsupervised machine learning algorithms. By importing the spectrogram data generated via FFT into SOMs, the features and correlation of the spectral data are mapped into the two-dimensional space, assisting in identifying and classifying various states in motor operation, from normal operation to different types of mechanical failures. As a powerful sequence model, LSTM has a memory and forgetting mechanism; it can effectively capture long-term dependencies in sequence data. The operational sound of the rotary motor is classified using the LSTM, and the effect of these data on the motor running state and failure mode is analyzed directly. This study also selected the orthogonal experiment design (OED) [28] to improve SOM-LSTM execution efficiency so that it has fast learning characteristics and robust analysis. Motor-fault diagnosis is an uncertain dynamic environment problem that involves many variables and restrictions. When the amount of prediction data is very large, problem solving will be more difficult. This paper proposes an SOM-LSTM calculation method and a sound conversion and OED mechanism to enhance the algorithm execution efficiency and increase the probability of finding the optimum solution so as to enhance the ability of the industrial circles to master motor operation information.