Motor fault detection using sound signature and wavelet transform

Abstract

The use of induction machines has gained fast popularity in many aspects of today’s energy applications and industrial productions. However, just as with any other machine, failure is expected due to a variety of faults in component and system levels. Therefore, it is necessary to improve machine reliability by performing preventive maintenance and exploring faulty indications in advance to avoid future failures. In normal operation, a distinct machine sound signature can be identify. Therefore, at any faulty operation, diagnosis of potential error can be defined based on output signature sound data analysis. Yet, this process of monitoring induction machine sounds and vibration can be hectic and extensive in terms of collecting data and compiling analysis. That is, a huge number of data samples need to be collected and stored in order to define abnormality operation. Therefore, in this work, wavelet-based algorithms were developed as an analysis process to analyze collected data and identify abnormality, with much fewer data samples and compiling process, as special prosperity of wavelet transform. As a result, MATLAB codes were implemented to analyze data based on sound signature technique and wavelet transform algorithms to show a significant improvement in identifying potential error and abnormality conditions.

Bearing fault diagnosis in induction motor using continuous wavelet transform and convolutional neural networks

“…Convolutional procedures are used to produce a more sophisticated feature representation by using input data to perform local feature extraction, reducing complexity and network parameters. The following is the convolution as (9).…”

“…This information allows for early detection and proactive measures to avoid unexpected downtime and expensive repairs. Signal processing tools, particularly in the diagnosis and prognosis stage of bearing health, play a vital role in maintenance [6]- [9]. Which time-frequency domain analysis method effectively extracts bearing vibration signal characteristics by decomposing them into frequency bands [10], [11].…”

Numerical Overcurrent Power Relay Enhancement Based on Integrated Discrete Wavelet Transform

“…While Fourier transform is used as a waveform visualizing tool, many other analysis applications are based on such algorithms. Yet, as Fourier analysis is localized in frequency domain and based on breaking the waveform into oscillations over the entire window analysis, local and temporal waveform data cannot be provided due to integral over time and limitation in frequency and bandwidth (Awada et al, 2021) especially for transient signals. In addition, as Fourier based on summation of cosine waves, noise, frequency distortion, harmonics and spectral impurity will be summed into the final waveform.…”

“…Yet, as disturbance caused by noise, harmonics, and frequency variation will be inherited as an error's factors into current waveform cycles calculations (Ray et al, 2017;Wei et al, 2020), and due to Fourier transform characteristics in localizing domain to provides magnitude and time, which is best used for stationary signals (Ahmad et al, 2018, Awada et al, 2016Awada, 2021;Elmore et al, 2015;Toufiq et al, 2021) relay effectiveness can be jeopardized and mis-leaded. As a result, many researchers have been investigating the benefit of wavelet transform to overcome these issues and detect power faults based on wavelet special features algorithm for current waveform analysis (Awada et al, 2021;Costa et al, 2015). In Seo, (2019), wavelet transform was used in the neural network of relay protection to identify and isolate the fault, however, the proposed technique is based on network layers and hierarchy process.…”

A non-invasive technique for monitoring supply voltage variation to single phase induction motor using doppler UWB radar signal analysis

“…The frequent voltage abnormalities cause for the malfunction of these devices, which in turn causes huge losses and low working hours. The possible failure of the IMs may be identified in advance by continuous monitoring of IMs various parameters such as vibration [5], acoustic [6], [7], flux, and eddy current, current signature [8]- [13]. Due to the variation of the supply voltage to the motors, the motor vibration amplitude changes.…”