Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals

Abstract

Vibration measurement and monitoring are essential in a wide variety of applications. Vibration measurements are critical for diagnosing industrial machinery malfunctions because they provide information about the condition of the rotating equipment. Vibration analysis is considered the most effective method for predictive maintenance because it is used to troubleshoot instantaneous faults as well as periodic maintenance. Numerous studies conducted in this vein have been published in a variety of outlets. This review documents data-driven and recently published deep learning techniques for vibration-based condition monitoring. Numerous studies were obtained from two reputable indexing databases, Web of Science and Scopus. Following a thorough review, 59 studies were selected for synthesis. The selected studies are then systematically discussed to provide researchers with an in-depth view of deep learning-based fault diagnosis methods based on vibration signals. Additionally, a few remarks regarding future research directions are made, including graph-based neural networks, physics-informed ML, and a transformer convolutional network-based fault diagnosis method.

Unmanned Surface Vehicle Thruster Fault Diagnosis via Vibration Signal Wavelet Transform and Vision Transformer under Varying Rotational Speed Conditions

“…Further research in FDD USVs also involved the application of dimension reduction and entropy-based numerical transformation techniques for each fault case [ 47 ]. The significance of vibration data was not only underscored in the context of FDD USV’s previous works but also broader applications involving the fault diagnosis of various rotational machines [ 26 ]. When factors such as propeller blade damage, temporary obstructions, or biological fouling occur, they cause a shift in the center of gravity away from the geometric center aligned with the propeller blade’s rotational axis.…”

“…Vibration sensors offer a cost-efficient alternative for acquiring diagnostic features when compared to other parameters such as power consumption, supply voltage, propeller blade rotation speed, and high-accuracy GNSS sentences. Fault diagnosis based on frequency analysis of vibration data has been frequently utilized in various fields due to its simplicity and the ability to observe the characteristics of different frequency components associated with faults [ 25 , 26 ]. The most commonly used thrust source for surface vehicles, the underwater thruster, is a type of rotating equipment that operates on rotational motion.…”

Highly Reliable Multicomponent MEMS Sensor for Predictive Maintenance Management of Rolling Bearings

“…In very general terms, applied procedures and methods for monitoring and early fault diagnosis of rolling bearings and related industrial processes are mainly supported by time–frequency–amplitude analysis of occurring vibrations [ 28 , 29 , 30 , 31 ], occasionally integrated with information on temperature fluctuations [ 32 , 33 ], noise levels [ 34 , 35 ] and other degradative harsh working environmental conditions affecting the functionality [ 36 , 37 , 38 , 39 ].…”

IInception-CBAM-IBiGRU based fault diagnosis method for asynchronous motors

“…However, asynchronous motors usually operate in complex environments where early fault features are often drowned out by noise so that faults are detected too late, resulting in a lag in repairing motor faults. Compared to machine learning-based methods that require manual extraction of features, which is time-consuming and labor-intensive, and the extracted features are susceptible to subjective factors, deep learning has the advantage of automatically extracting feature information from raw data and learning the intrinsic laws of the data by exploring the association between data and faults to perform fault diagnosis on equipment 3 . One of the deep learning is the convolutional neural network (CNN), a supervised deep learning algorithm with a convolutional structure with good self-learning, fault tolerance, and parallel processing capabilities.…”