Distributed neuro-fuzzy feature forecasting approach for condition monitoring

Abstract

The industrial machinery reliability represents a critical factor in order to assure the proper operation of the whole productive process. In regard with this, diagnosis schemes based on physical magnitudes acquisition, features calculation, features reduction and classification are being applied. However, in this paper, in order to enhance the condition monitoring capabilities, a forecasting approach is proposed, in which not only the current status of the system under monitoring in identified, diagnosis, but also the future condition is assessed, prognosis. The novelties of the proposed methodology are based on a distributed features forecasting approach by means of adaptive neuro-fuzzy inference system models. The proposed method is validated by means of an accelerated bearing degradation experimental platform.Postprint (published version

Artificial intelligence in prognostics and health management of engineering systems

“…The guidance for using Table 2 is to mainly serve as quick pointers to publications in which specific AI algorithms have been used in the literature for PHM so as to gain further insight into a specific approach or to aid comparison of research results. (Hong and Zhou, 2012;Baraldi et al, 2015;Aye and Heyns, 2017;Richardson et al, 2017); Sparse Bayesian Learning (Zhou et al, 2012); Adaptive neuro-fuzzy inference system -ANFIS (Zurita et al, 2014); Instance-based learning (Khelif et al, 2014); Kalman Filter (Singleton et al, 2015;Son et al, 2016;Cui et al, 2020); k-NN (Xiong et al, 2015); Particle Filter (Guha et al, 2016;Miao et al, 2013;Su et al, 2017;Chang and Fang, 2019); PCA (Yongxiang et al, 2016); Hidden semi-Markov model (Zhu and Liu, 2018); Light gradient boosting machine ; Sparse coding (Ren and Lv, 2016). Some of the algorithms appearing as being used in only one publication may actually have been used in multiple publications but have been grouped under fusion, hybrid or comparison approaches.…”

Marine Systems and Equipment Prognostics and Health Management: A Systematic Review from Health Condition Monitoring to Maintenance Strategy

“…Hong et al determined four different degradation stages according to the confidence value (CV) of health state and the change rate of CV and developed different prediction models according to different HSs [195]. In addition, the clustering algorithm [196][197][198] and the AI classification method [199][200][201][202][203] are also applicable to the division of different HSs and have achieved good results. A single model is not enough to express its degradation process when performing failure analysis and RUL prediction on equipment with three or more HSs.…”

Application of Machine Learning Tools for Long-Term Diagnostic Feature Data Segmentation

“…The speed of the shaft was kept stable during the test. It is assumed that the failure of the bearing occurs when the amplitude of the vibration signal has arrived above 20 g. This data set was used in many publication for segmentation [75][76][77][78][79], construction of the health index [80][81][82][83][84][85][86][87] and predicting RUL [88][89][90][91][92][93]. As one can see, this data set perfectly follows the idea of three regimes: for time 0 to c.a.…”