An optimized stacked diagnosis structure for fault diagnosis of wind turbine planetary gearbox

Abstract

Fault diagnosis of the planetary gearbox (PGB) of wind turbines (WTs) plays an important role in the normal operation of WTs. Current studies commonly focus on the diagnosis of fault types of WT PGBs. Nevertheless, in addition to identifying the fault type, the current severity of the fault is also instructive for the maintenance and repair of WT PGBs. Thus, a novel optimized stacked diagnosis structure (OSDS) is proposed for the identification of fault type and severity. Compressed sensing is adopted to implement the compressed sampling of original vibration signals collected by the wireless sensor. Then, the compressed samples are input into first- and second-layer deep belief networks (DBNs) for the separate identification of fault type and severity. In order to realize the best feature extraction performance of DBNs, every single DBN in the OSDS is optimized with the chaotic quantum particle swarm optimization (CQPSO) algorithm. For OSDS, which is a hierarchical diagnosis system, the misdiagnosis results of the first layer will bring irreversible influence to the diagnosis of the second layer. That is to say, an incorrect fault type diagnosis will mean that these signals are wrongly classified, making them unable to judge the severity of the fault. Because the first-layer DBN is optimized with PGB historical data and the CQPSO algorithm, it shows an excellent performance in identifying fault types. Therefore, the diagnostic performance of OSDS has not been affected by the absence of diagnosis, and still shows an excellent recognition performance of fault type and severity in the experiment. This verifies its excellent role in the fault diagnosis of WT PGBs.

Robust fault diagnosis of quayside container crane gearbox based on 2D image representation in frequency domain and CNN

“…The gearbox dataset of Case 1 is provided by the University of Connecticut (UoC), 39,40 which has been extensively used to explore the performance of fault diagnosis methods. [41][42][43][44][45][46][47][48] In addition, Zhao et al 41 tested seven publicly available datasets through four benchmark types of deep learning models and showed that the UoC gearbox dataset is the most difficult to diagnose among the seven datasets. The gearbox data of Case 1 is used to study the universality and progressiveness of the proposed method.…”

Fault diagnosis of bearing-rotor system based on infrared thermography: ReSPP with multi-scaled training method

“…Fault diagnosis methods mainly include vibration signalbased methods [6][7][8][9] and thermal signal-based methods [10,11]. The vibration signal has high accuracy, it is the most accessible signal in research and applications.…”

“…The precision rate, recall rate, and F-score of this run are shown in figure12. In fault diagnosis, precision rate and recall rate are evaluation metrics, represented respectively as follows: Precision = TP/(TP + FP)(6) Recall = TP/(TP + FN)(7)…”

Interpretability of deep convolutional neural networks on rolling bearing fault diagnosis

“…In the past decades, rotating machines have been widely applied in modern industries [1]. Machinery failures during operations severely compromise safety and lead to large maintenance costs [2,3]. Effective and accurate machine health condition monitoring and fault diagnosis have always been in high demand.…”