Electric traction motors in automotive applications work in operational conditions

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Abstract

This work presents a method to improve the diagnostic performance of empirical classification system (ECS), which is used to estimate the degradation state of components based on measured signals. The ECS is embedded in a homogenous continuous-time, finite-state semiMarkov model (HCTFSSMM), which adjusts diagnoses based on the past history of components. The combination gives rise to a homogeneous continuous-time finite-state hidden semi-Markov model (HCTFSHSMM). In an application involving the degradation of bearings in automotive machines, the proposed method is shown to be superior in classification performance compared to the single-stage ECS.

Data-driven methods for direct prognostic map the relationship between monitored parameters and equipment Remaining Useful Life (RUL). They typically require the availability of a set of run-to-failure degradation trajectories for model training. Yet, in many industrial applications, equipment is often replaced before they fail to avoid catastrophic consequences on production and safety. Then also, incomplete degradation trajectories are available. In this work, we develop a method for predicting equipment RUL, and the related uncertainty based on both complete and incomplete degradation trajectories. The method is based on the combined use of a similarity measure and Evidence Theory (EvT). Application of the method on two case studies shows that it provides accurate RUL predictions, also in comparison with a similarity-based regression method of literature.

Multi-State (MS) reliability models are used in practice to describe the evolution of degradation in industrial components and systems. To estimate the MS model parameters, we propose a method based on the Fuzzy Expectation-Maximization (FEM) algorithm, which integrates the evidence of the field inspection outcomes with information taken from the maintenance operators about the transition times from one state to another. Possibility distributions are used to describe the imprecision in the expert statements. A procedure for estimating the Remaining Useful Life (RUL) based on the MS model and conditional on such imprecise evidence is, then, developed. The proposed method is applied to a case study concerning the degradation of pipe welds in the coolant system of a Nuclear Power Plant (NPP). The obtained results show that the combination of field data with expert knowledge can allow reducing the uncertainty in degradation estimation and RUL prediction.

Big data from very large fleets of assets challenge the asset management

No abstract