An intelligent fault diagnosis system for newly assembled transmission

Application of psychoacoustics for gear fault diagnosis using artificial neural network

“…However, very limited work can be been found for evaluating gearbox quality using psychoacoustics to identify correct working of gearbox during end of line inspection. Shang et al 25 have also reported similar situation of dependence on human hearing ability and expertise in gearbox inspection after making the assembly. They have proposed vibration based technique which makes use of time and frequency domain methods.…”

“…While comparing the results of this paper with other published work and the conventional vibration based condition monitoring techniques, following observations are made. Shang et al 25 had carried out similar work for fault identification during end of line inspection using vibration based technique. They have highlighted the limitations of the various vibration based condition monitoring techniques such as HHT, wavelet transform, and envelop spectrum for fault identification in newly manufactured gearbox.…”

Study on a Novel Hybrid Intelligent Fault Diagnosis Method Based on Improved DE and RBFNN

“…Wang et al [30] proposed a noise-based intelligent method based on the techniques of Hilbert-Huang transform (HHT) and support vector machine (SVM) for engine fault diagnosis (EFD), so-called HHT-SVM mode. Shang, et al [31] proposed a new automatic fault detection method for automobile transmission and a fault diagnosis expert system for newly assembled transmission, related method of knowledge representation, feature extraction and fault classification is given. Zhang, et al [32] proposed a novel fault diagnosis strategy based on rotor dynamics and computational intelligence in order to achieve accurate diagnosis for rotating machinery automatically and obtain test data under actual fault conditions.…”

Correlated and weakly correlated fault detection based on variable division and ICA

“…A huge amount of data including useful information can be collected (Huang & Yan, 2016b;Yan, Chen, Yao, & Huang, 2016). Fault detection is an important task as it is necessary in ensuring the stable production safety, maintaining quality stabilisation, and optimising production profit (Harrou, Kadri, Chaabane, Tahon, & Sun, 2015;Jiang, Song, & Ge, 2017;Shang, Zhou, & Yuan, 2014;Sun & Zhang, 2017;Xie, Sun, & Li, 2015;Zhang, Qi, Wang, Gao, & Wang, 2017;Zhong, Zhang, Ding, & Zhou, 2017). In order to ensure reliability and stabilisation, it is vital to develop effective fault detection methods for real industrial processes (Luo, Lovelett, & Ogunnaike, 2017;Maleki, Amiri, & Castagliola, 2017;Peng, Tang, Du, & Qian, 2017;Shang, Chen, & Ji, 2017;Wang, Yan, & Jiang, 2016;Zhang, Ren, Yao, Zou, & Wang, 2015).…”