# An Audio-Based Motor-Fault Diagnosis System with SOM-LSTM
###### Abstract  
This paper combines self-organizing mapping (SOM) and a long short-term memory network (SOM-LSTM) to construct an audio-based motor-fault diagnosis system for identifying the operating states of a rotary motor. This paper first uses an audio signal collector to measure the motor sound signal data, uses fast Fourier transform (FFT) to convert the actual measured sound-time-domain signal into a frequency-domain signal, and normalizes and calibrates the frequency-domain signal to ensure the consistency and accuracy of the signal. Secondly, the SOM is used to further analyze the characterized frequency-domain waveforms in order to reveal the intrinsic structure and pattern of the data. The LSTM network is used to process the secondary data generated via SOM. Dimensional data aggregation and the prediction of sequence data in long-term dependencies accurately identify different operating states and possible abnormal patterns. This paper also uses the experimental design of the Taguchi method to optimize the parameters of SOM-LSTM in order to increase the execution efficiency of fault diagnosis. Finally, the fault diagnosis system is applied to the real-time monitoring of the motor operation, the work of identifying the motor-fault type is performed, and tests under different loads and environments are attempted to evaluate its feasibility. The completion of this paper provides a diagnostic strategy that can be followed when it comes to motor faults. Through this fault diagnosis system, abnormal conditions in motor equipment can be detected, which can help with preventive maintenance, make work more efficient and save a lot of time and costs, and improve the industry's ability to monitor motor operation information. m +
Footnote †: journal: Applied  
m +
Footnote †: journal: Applied  
In the early days, most studies on motor faults still used vibration analysis methods [6]. The purpose of fast Fourier transform is to process the vibration signal and analyze the spectrum of the signal to identify various types of faults [7]. However, when using FFT to analyze time-domain signals into frequency-domain signals, signal distortion sometimes occurs and is not yet perfect. In some diagnoses, the obscure feature parameters result in misjudgments of the detection system. Additionally, the fixed size of the chosen window, the difficulties in quantifying the faults' extent, and the high computational cost required to obtain a good resolution still remain the major drawbacks of this technique [8; 9]. In recent years, artificial neural networks (ANNs) have been successfully used in fault prediction; motor-fault problems are solved more simply, effectively, and flexibly [10; 11; 12]. The usual approach to applying ANNs is to obtain fault identification and extract features that are learned by mapping machine conditions to the extracted features. However, ANNs have essential weaknesses, it is not easy to determine the size of the network architecture and related parameters in a dynamic environment [13]. Therefore, building a sound-based motor-fault diagnosis system to improve power maintenance personnel's capability for motor-fault diagnosis is a direction of future work. In the research on motor-fault detection, ANN is the most commonly intelligent tool. Especially when monitoring the condition of motors, using ANN-appropriate technology and reliable algorithms can avoid unexpected failures in industrial processes. Ref. [14] used common fault types for induction motors, such as compound faults for rotor imbalance, stator short circuit, and rotor, as the objectives of machine learning diagnoses and judgments. Ref. [15] used the correlation vector formed by the single fault type and the corresponding spectral characteristic relation to decompose the entire correlation matrix into independent correlation vectors and combined the statistical parameters of time-domain vibration signals with a Bayesian network for gear fault diagnosis. Ref.