An Automated Data Fusion-Based Gear Faults Classification Framework in Rotating Machines

Abstract

The feasibility and usefulness of frequency domain fusion of data from multiple vibration sensors installed on typical industrial rotating machines, based on coherent composite spectrum (CCS) as well as poly-coherent composite spectrum (pCCS) techniques, have been well-iterated by earlier studies. However, all previous endeavours have been limited to rotor faults, thereby raising questions about the proficiency of the approach for classifying faults related to other critical rotating machine components such as gearboxes. Besides the restriction in scope of the founding CCS and pCCS studies on rotor-related faults, their diagnosis approach was manually implemented, which could be unrealistic when faced with routine condition monitoring of multi-component industrial rotating machines, which often entails high-frequency sampling at multiple locations. In order to alleviate these challenges, this paper introduced an automated framework that encompassed feature generation through CCS, data dimensionality reduction through principal component analysis (PCA), and faults classification using artificial neural network (ANN). The outcomes of the automated approach are a set of visualised decision maps representing individually simulated scenarios, which simplifies and illustrates the decision rules of the faults characterisation framework. Additionally, the proposed approach minimises diagnosis-related downtime by allowing asset operators to easily identify anomalies at their incipient stages without necessarily possessing vibration monitoring expertise. Building upon the encouraging results obtained from the preceding part of this approach that was limited to well-known rotor-related faults, the proposed framework was significantly extended to include experimental and open-source gear fault data. The results show that in addition to early established rotor-related faults classification, the approach described here can also effectively and automatically classify gearbox faults, thereby improving the robustness.

Sensor-Based Predictive Maintenance with Reduction of False Alarms—A Case Study in Heavy Industry

“…In the context of vibration-based predictive maintenance, data fusion has recently become a more frequently addressed issue. Various studies show how different data fusion approaches [ 37 , 38 , 39 , 40 ] can be used and how they affect the fault diagnosis of gearboxes and rotating machinery in general. Research on the application of data fusion methods for the approach presented in this work is one of the possible directions for future work.…”

Development of Intelligent Fault Diagnosis Technique of Rotary Machine Element Bearing: A Machine Learning Approach

“…Although it was published in 2012, its contribution is still considered a remarkable one. Cao et al developed a gear fault detection model with data fusion in 2021 [ 33 ]. Carlos et al proposed a vibration-based fault diagnosis technique for induction motors using the orthogonal matching pursuit algorithm [ 34 ].…”

Fault Diagnosis Algorithm of Gearbox Based on NAPSO-VMD Self-Adaptive Noise Reduction and Dual-Sensor Feature Fusion

“…On the other hand, there is redundant information between multisensor signals, and the accuracy and effect of equipment fault diagnosis can be improved through effective information fusion. In this regard, Xie et al [25] used principal component analysis (PCA) to fuse and convert multisensor signal features into RGB images, and then, the image samples are input into a convolutional neural network (CNN) with residuals for further extraction of deep features; Li et al [26] proposed an adaptive channel weighted neural network to study the importance of different sensor signals in the feature fusion method while maximizing the mining of the deep fault feature information of each sensor and finally realized the condition monitoring of the gearbox transmission system and the helicopter transmission system; Cao and Yunusa-Kaltungo [27] proposed a gearbox fault classification framework for the automatic fusion of multisensor data, generating features through coherent composite spectros-copy (CCS) and using PCA for data dimensionality reduction. The final diagnosis results were obtained from artificial neural network training feature samples.…”