Abstract
Today, real-time fault detection and predictive maintenance based on sensor data are actively introduced in various areas such as manufacturing, aircraft, and power system monitoring. Many faults in motors or rotating machinery like industrial robots, aircraft engines, and wind turbines can be diagnosed by analyzing signal data such as vibration and noise. In this study, to detect failures based on vibration data, preprocessing was performed using signal processing techniques such as the Hamming window and the cepstrum transform. After that, 10 statistical condition indicators were extracted to train the machine learning models. Specifically, two types of Mahalanobis distance (MD)-based one-class classification methods, the MD classifier and the Mahalanobis–Taguchi system, were evaluated in detecting the faults of rotating machinery. Their performance for fault detection on rotating machinery was evaluated with different imbalanced ratios of data by comparing with binary classification models, which included classical versions and imbalanced classification versions of support vector machine and random forest algorithms. The experimental results showed the MD-based classifiers became more effective than binary classifiers in cases in which there were much fewer defect data than normal data, which is often common in the real-world industrial field.
Highlights
In manufacturing industry, there is much interest in smart manufacturing to improve productivity and competitiveness
We evaluated two Mahalanobis distance (MD)-based classification methods, MDCIR was higher, the overall classification performances turned lower
To use the vibraIR = 3.333, cost-sensitive SVM (CS_SVM) showed similar performance to MDC and less than MTS, but support vector machines (SVM), random forest (RF), tion data for analysis, they were by applying signal 6.667, processing techniques and hadpreprocessed lower performances
Summary
In manufacturing industry, there is much interest in smart manufacturing to improve productivity and competitiveness. The smart manufacturing is realized using advanced technologies such as the Internet of Things (IoT), artificial intelligence, and big data analysis [1]. Complex facilities in manufacturing systems need to be monitored and maintained in more sophisticated manners. To this end, the prognostics and health management (PHM) technology is capable of diagnosing or predicting faults by detecting or analyzing the condition of facilities using IoT, machine learning and big data analytics. Rotating machinery such as industrial motors, aircraft engines, and wind turbines are playing crucial roles in the automation of manufacturing systems. The fault detection of rotating machines has a decisive influence on system productivity. The failure of a rotating machine that transmits power to various facilities results in great economic loss due to the performance degradation or shutdown of the system
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