High-speed train wheel set bearing fault diagnosis and prognostics: A new prognostic model based on extendable useful life. (1st January 2021) - Xu, Guanji
Highlights: Introduced a test platform that has set a close to reality high-speed train operation environment. Statistical properties of several AE and vibration parameters used for bearing status estimation are studied. Discussed bearing status quantification approaches with speed independent signature and geometric model. Proposed a new model finds industrial viable extendable useful life (EUL) instead of seeking idealistic remaining useful life (RUL) for prognostics. Abstract: Diagnosis and prognostics of rolling element bearings have been widely studied in recent years, but very few researches were dealing with high-speed train wheel set bearings (HSTWSB). Most prognostics and health management (PHM) models are generally based on obtaining the remaining useful life (RUL) of concerned bearings. Since it is difficult to quantify and to monitor bearing status from vibration signal and there is no clear definition what is the end of bearing service life, determine RUL is not realistic in industrial practice. In order to achieve reliable fault diagnosis and prognosis for HSTWSB, it is of great importance and necessity to conduct a thorough research under realistic or close to reality operation conditions. Therefore, in this paper two types of techniques, i.e. vibration and acoustic emission, have been particularly studied. Different from many previous PHM studies which seek seeking bearing's RUL by establishing physics model or artificial neural network model, a new hybrid model based on extendable useful life (EUL) under continuous monitoring and bearing status classification is proposed. Statistical properties of typical time domain features extracted from vibration and acoustic emission are studied. Correlations of these parameters with bearing status are reviewed and feasible parameters are evaluated for bearing status quantification. By driving an electric multiple unit (EMU) speed up to 350 km/h, a test device close to real running environment was introduced. A batch of bearings with different level of nature defects instead of artifacts were particularly selected as database samples of this paper. Test procedure was designed to allow fault diagnosis to be verified under low, medium and high speeds and the corresponding database and knowledgebase of bearing status assessment are established. Defect geometries were quantified with 3D laser scanning technology so that it provides intuitive references for evaluating effectiveness of signal processing approaches with respective to bearing damage status. Instead of calculating how much RUL left by physics model or neural network model, the proposed approach determines if the useful life can be extended from one grade level to another or to next overhaul under continuous monitoring. The proposed model establishes an initial database and knowledgebase for HSTWSB monitoring. This model can be dynamically enhanced with involvement of AI technology and accumulation of tested bearing database in the future.