scholarly journals Decentralized Fault Diagnosis and Prognosis Scheme for Interconnected Nonlinear Discrete-Time Systems

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
H. Ferdowsi ◽  
S. Jagannathan
Keyword(s):  
Fault Diagnosis ◽  
Discrete Time ◽  
Fault Isolation ◽  
Remaining Useful Life ◽  
Detection Scheme ◽  
Diagnosis And Prognosis ◽  
Discrete Time Systems ◽  
Local Measurements ◽  
Time Systems ◽  
Fault Diagnosis And Prognosis

This paper deals with the design of a decentralized fault diagnosis and prognosis scheme for interconnected nonlinear discrete-time systems which are modelled as the interconnection of several subsystems. For each subsystem, a local fault detector (LFD) is designed based on the dynamic model of the local subsystem and the local states. Each LFD consists of an observer with an online neural network (NN)-based approximator. The online NN approximators only use local measurements as their inputs, and are always turned on and continuously learn the interconnection as well as possible fault function. A fault is detected by comparing the output of each online NN approximator with a predefined threshold instead of using the residual. Derivation of robust detection thresholds and fault detectability conditions are also included. Due to interconnected nature of the overall system, the effect of faults propagate to other subsystems, thus a fault might be detected in more than one subsystem. Upon detection, faults local to the subsystem and from other subsystems are isolated by using a central fault isolation unit which receives detection time information from all LFDs. The proposed scheme also provides the time-to-failure or remaining useful life information by using local measurements. Simulation results provide the effectiveness of the proposed decentralized fault detection scheme.

Uncertainty Management in Lebesgue Sampling-Based Fault Diagnosis and Prognosis

2016 ◽  
Author(s):  
Wuzhao Yan ◽  
Bin Zhang
Keyword(s):  
Fault Diagnosis ◽  
Particle Filtering ◽  
Uncertainty Management ◽  
Remaining Useful Life ◽  
Measurement Noise ◽  
Diagnostic Model ◽  
Contributing Factors ◽  
Diagnosis And Prognosis ◽  
Short Term Prediction ◽  
Fault Diagnosis And Prognosis

This paper develops the uncertainty management of fault diagnosis and prognosis (FDP) in Lebesgue sampling (LS)-based framework with an application to helicopter drivetrain gearbox. In the developed LS-based FDP system, a particle filtering-based FDP algorithm, fault diagnostic model, failure prognostic model, and uncertainty management are discussed. Although uncertainty management has been developed in the traditional Riemann sampling (RS)-based FDP, it needs to be analyzed and managed in a totally different way since the working principle of LS-FDP is fundamentally different from that of RS-FDP. Inaccurate model structure and parameter, measurement noise, process noise, and unknown future loading are major contributing factors of uncertainties in LS-FDP framework. Since the noise in LS-based prognosis is a distribution on time axis while the noise in RS-based prognosis is one on fault state axis, this paper studies the transpose of noise distribution from state domain to time domain. In order to reduce the uncertainty in the prediction of remaining useful life (RUL), model noise and measurement noise terms are adjusted based on a short-term prediction with n steps and correction loop. In this scheme, the priori time distribution at the (t + n)-th Lebesgue state is predicted and stored at the t-th Lebesgue state. Then, at the (t + n)-th Lebesgue state, when the posteriori distribution becomes available, it is compared with the stored priori distribution to manage the uncertainty. The methods for uncertainty management are illustrated by a case study of the prediction of RUL of gearbox. The experimental results show that the uncertainty in the diagnosis and prognosis process of gearbox is properly managed and the confidence interval is decreased, which enhances the confidence level for decision-making and condition-based maintenance.

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