Advancement of Fault Diagnosis and Detection Process in Industrial Machine Environment

Abstract

Machine fault diagnosis is a very important topic in industrial systems and deserves further consideration in view of the growing complexity and performance requirements of modern machinery. Currently, manufacturing companies and researchers are making a great attempt to implement efficient fault diagnosis tools. The signal processing is a key step for the machine condition monitoring in complex industrial rotating electrical machines. A number of signal processing techniques have been reported from last two decades conventionally and effectively applied on different rotating machines. Induction motor is the one of widely used in various industrial applications due to small size, low cost and operation with existing power supply. Faults and failure of the induction machine in industry can be the cause of loss of throughput and significant financial losses. As compared with the other faults with the broken rotor bar, it has significant importance because of severity which leads to a serious breakdown of motor. Detection of rotor failure has become significant fault but difficult task in machine fault diagnosis. The aim of this paper is indented to summarizes the fault diagnosis techniques with the purpose of the broken rotor bar fault detection. Keywords: machine fault diagnosis, signal processing technique, induction motor, condition monitoring.

Machine Health Monitoring and Fault Diagnosis Techniques Review in Industrial Power-Line Network

“…To turn waveform data into information, fault condition indicators (features) are extracted and/or selected from the acquired signals. Reliable features generally have the following characteristics [8,9]: After acquiring the spectrum data, different types of signal processing methods have been utilised to extract useful feature information and interpret signal waveform data for further fault diagnosis purposes in motors. Most feature extraction techniques can be divided into three groups, as shown in Figure 1:…”

“…A comparison of FFT, STFT and continuous wavelet transform (CWT) methods [9][10][11] is summarised in Table 1.…”

Condition-Based Health Monitoring of Electrical Machines Using DWT and LDA Classifier

“…Here, this study uses the MCSA technique for the prediction of common motor faults that appear in running motors. The MCSA is the most common [4], reliable [5][6][7], and efficient technique for diagnosing faults in electrical machines [8][9][10][11]. It provides a good understanding of the MCSA.…”