A Particle Filtering Framework for Failure Prognosis

2005 ◽  
Author(s):  
Marcos Orchard ◽  
Biqing Wu ◽  
George Vachtsevanos
Keyword(s):  
Learning Strategies ◽  
Particle Filtering ◽  
Measurement Model ◽  
Remaining Useful Life ◽  
Diagnosis And Prognosis ◽  
Novel Approach ◽  
Posterior Probability Density ◽  
Failure Prognosis ◽  
Fault Diagnosis And Prognosis

Bayesian estimation techniques are finding application domains in machinery fault diagnosis and prognosis of the remaining useful life of a failing component/subsystem. This paper introduces a methodology for accurate and precise prediction of a failing component based on particle filtering and learning strategies. This novel approach employs a state dynamic model and a measurement model to predict the posterior probability density function of the state, i.e., to predict the time evolution of a fault or fatigue damage. It avoids the linearity and Gaussian noise assumption of Kalman filtering and provides a robust framework for long-term prognosis while accounting effectively for uncertainties. Correction terms are estimated in a learning paradigm to improve the accuracy and precision of the algorithm for long-term prediction. The proposed approach is applied to a crack fault and the results support its robustness and superiority.

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.

scholarly journals Novel Waveforms, Models, Algorithms for Cable Health Monitoring

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Xuan Wang
Keyword(s):  
Particle Filtering ◽  
Degradation Mechanism ◽  
Accelerated Aging ◽  
Remaining Useful Life ◽  
Cable Insulation ◽  
Diagnosis And Prognosis ◽  
Accelerated Aging Test ◽  
Aging Test ◽  
Insulation Degradation ◽  
Fault Diagnosis And Prognosis

Cables in power generation and delivery are under high thermal stress cycles. Such high temperature can lead to cable insulation degradation, which will reduce the projected lifetime. Existing methods mainly focus on cable fault detection or insulation degradation mechanism. There is no existing tools for diagnosing the insulation degradation level and predicting the remaining useful life of the cable. The goal of my Ph.D. research is to develop reflectometry and data based approaches to monitor the health status of cables. The research will be conducted in three steps: (1) development of reflectometry based method to monitor the cable insulation degradation; (2) feature extraction and cable insulation degradation dynamic modeling based on the accelerated aging test data; (3) development of risksenstive particle filtering based fault diagnosis and prognosis algorithms for cable degradation; and (4) verification and validation the proposed solution with new experiment data and comparison with existing approaches.

scholarly journals Fault Diagnosis and Prognosis Based on Deep Belief Network and Particle Filtering

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Guangxing Niu ◽  
Shije Tang ◽  
Zhichao Liu ◽  
Guangquan Zhao ◽  
Bin Zhang
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Particle Filtering ◽  
Learning Algorithms ◽  
Remaining Useful Life ◽  
Health State ◽  
Diagnosis And Prognosis ◽  
Fault Dynamics ◽  
Useful Life ◽  
Fault Diagnosis And Prognosis

Fault diagnosis and prognosis (FDP) plays more and more important role in industries FDP aims to estimate current fault condition and then predict the remaining useful life (RUL). Based on the estimation of health state and RUL, essential decisions on maintenance, control, and planning can be conducted optimally in terms of economy, efficiency, and availability. With the increase of system complexity, it becomes more and more difficult to model the fault dynamics, especially for multiple interacting fault modes and for fault modes that are affected by many internal and external factors. With the development of machine learning and big data, deep learning algorithms become important tools in FDP due to their excellent performance in data processing, information extraction, and automatic modeling. In the past a few years, deep learning algorithms demonstrate outstanding performance in feature extraction and learning fault dynamics. As emerging techniques, their powerful learning capabilities attract more and more attentions and have been extended to various applications. This work presents a novel diagnosis and prognosis methodology which combined deep belief networks (DBNs) and Bayesian estimation. In the proposed work, the DBNs are trained offline using available historical data. The fault dynamic model is then represented by the trained DBNs and modeling uncertainty is described by noise. The integration of DBNs with particle filtering is then developed to provide an estimation of the current fault state and predict the remaining useful life, which is very suitable and efficient for most nonlinear fault models. Experimental studies of lithium-ion batteries are presented to verify the effectiveness of the proposed solution.

scholarly journals Special Issue “Advances in Machine Learning and Deep Learning Based Machine Fault Diagnosis and Prognosis”

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 532
Author(s):  
Mohand Djeziri ◽  
Marc Bendahan
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Special Issue ◽  
Maintenance Strategies ◽  
Corrective Maintenance ◽  
Diagnosis And Prognosis ◽  
Machine Fault Diagnosis ◽  
Failure Prognosis ◽  
Fault Diagnosis And Prognosis

Fault diagnosis and failure prognosis aim to reduce downtime of the systems and to optimise their performance by replacing preventive and corrective maintenance strategies with predictive or conditional ones [...]

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.

scholarly journals Improving Study Skills by Combining a Study Skill Module and Repeated Reflection Seminars

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Björn Hedin ◽  
Viggo Kann
Keyword(s):  
Learning Strategies ◽  
Study Skills ◽  
Learning Study ◽  
Learning To Learn ◽  
Learning Skills ◽  
Self Regulated Learning ◽  
Novel Approach ◽  
Regulated Learning ◽  
Skills Learning

If students have a broad spectrum of study skills, learning will likely be positively affected, since they can adapt the way they learn in different situations. Such study skills can be learned in, for example, learning-to-learn courses. Several studies of such courses have been done over the years, but few of these have been carried out in longitudinal naturalistic settings, where the effect has been evaluated over several years in nonexperimental settings. In this paper, we present a novel approach for learning study skills, as a part of a course running over three years. The course starts with a learning-to-learn module, followed by 11 follow-ups that include, among other things, peer discussions about learning strategies with the aim of promoting self-regulated learning. This evaluation shows which study skills the students were most interested in trying, how successful they were in continuing to use the study skills, and which effects the students believed the study skills had after trying them. No significant change was found in how satisfied the students were with their overall study technique immediately after the initial module, but in the long term, 78% of the students believed the course had promoted their ability to analyze and adapt their study habits. We conclude that our approach could be a useful way to get the students to improve their repertoire and use of study skills, and we believe that the students also will improve general self-regulated learning skills.

scholarly journals Detection and Prognosis of Propagating Faults in Flight Control Actuators for Helicopters

Aerospace ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 20 ◽  
Author(s):  
Andrea Nesci ◽  
Andrea De Martin ◽  
Giovanni Jacazio ◽  
Massimo Sorli
Keyword(s):  
Flight Control ◽  
Particle Filtering ◽  
Failure Modes ◽  
Selection Process ◽  
Critical Systems ◽  
Maintenance Policy ◽  
Prognostic System ◽  
Diagnosis And Prognosis ◽  
Failure Prognosis ◽  
Depth Feature

Recent trend in the aeronautic industry is to introduce a novel prognostic solution for critical systems in the attempt to increase vehicle availability, reduce costs, and optimize the maintenance policy. Despite this, there is a general lack of literature about prognostics for hydraulic flight control systems, especially looking at helicopter applications. The present research was focused on a preliminary study for an integrated framework of fault detection and failure prognosis tailored for one of the most common architectures for flight control actuation. Starting from a high-fidelity dynamic model of the system, two different faults were studied and described within a dedicated simulation environment: the opening of a crack in the coils of the centering springs of the actuator and the wear of the inner seals. Both failure modes were analyzed through established models available in the literature and their evolution simulated within the model of the actuator. Hence, an in-depth feature selection process was pursued aimed at the definition of signals suitable for both diagnosis and prognosis. Results were then reported through an accuracy-sensitivity plane and used to define a prognostic routine based on particle filtering techniques. The more significant contribution of the present research was that no additional sensors are needed so that the prognostic system can be potentially implemented for in-service platforms.

scholarly journals Particle Filtering Based Remaining Useful Life Prediction for Electromagnetic Coil Insulation

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 473
Author(s):  
Haifeng Guo ◽  
Aidong Xu ◽  
Kai Wang ◽  
Yue Sun ◽  
Xiaojia Han ◽  
...  
Keyword(s):  
Particle Filtering ◽  
Remaining Useful Life ◽  
Electrical Parameters ◽  
Degradation Model ◽  
Accelerated Degradation ◽  
Insulation Failure ◽  
Electromagnetic Coils ◽  
Degradation Monitoring ◽  
Insulation Degradation ◽  
Useful Life

Electromagnetic coils are one of the key components of many systems. Their insulation failure can have severe effects on the systems in which coils are used. This paper focuses on insulation degradation monitoring and remaining useful life (RUL) prediction of electromagnetic coils. First, insulation degradation characteristics are extracted from coil high-frequency electrical parameters. Second, health indicator is defined based on insulation degradation characteristics to indicate the health degree of coil insulation. Finally, an insulation degradation model is constructed, and coil insulation RUL prediction is performed by particle filtering. Thermal accelerated degradation experiments are performed to validate the RUL prediction performance. The proposed method presents opportunities for predictive maintenance of systems that incorporate coils.

Development of a Performance-Related Framework for Asphalt Mixture Design for the Illinois Tollway

2021 ◽  
pp. 036119812110148
Author(s):  
Behnam Jahangiri ◽  
Punyaslok Rath ◽  
Hamed Majidifard ◽  
William G. Buttlar
Keyword(s):  
Asphalt Mixture ◽  
Field Performance ◽  
Compact Tension ◽  
T Test ◽  
Novel Approach ◽  
Final Adjustment ◽  
Development And Validation ◽  
High Degree ◽  
Selection Of

Various agencies have begun to research and introduce performance-related specifications (PRS) for the design of modern asphalt paving mixtures. The focus of most recent studies has been directed toward simplified cracking test development and evaluation. In some cases, development and validation of PRS has been performed, building on these new tests, often by comparison of test values to accelerated pavement test studies and/or to limited field data. This study describes the findings of a comprehensive research project conducted at Illinois Tollway, leading to a PRS for the design of mainline and shoulder asphalt mixtures. A novel approach was developed, involving the systematic establishment of specification requirements based on: 1) selection of baseline values based on minimally acceptable field performance thresholds; 2) elevation of thresholds to account for differences between short-term lab aging and expected long-term field aging; 3) further elevation of thresholds to account for variability in lab testing, plus variability in the testing of field cores; and 4) final adjustment and rounding of thresholds based on a consensus process. After a thorough evaluation of different candidate cracking tests in the course of the project, the Disk-shaped Compact Tension—DC(T)—test was chosen to be retained in the Illinois Tollway PRS and to be presented in this study for the design of crack-resistant mixtures. The DC(T) test was selected because of its high degree of correlation with field results and its excellent repeatability. Tailored Hamburg rut depth and stripping inflection point thresholds were also established for mainline and shoulder mixes.

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