Journal ArticleDOI
Machine Condition Prediction Based on Adaptive Neuro–Fuzzy and High-Order Particle Filtering
TLDR
A new prognostic method is developed using adaptive neuro-fuzzy inference systems (ANFISs) and high-order particle filtering that outperforms classical condition predictors.Abstract:
Machine prognosis is a significant part of condition-based maintenance and intends to monitor and track the time evolution of a fault so that maintenance can be performed or the task can be terminated to avoid a catastrophic failure. A new prognostic method is developed in this paper using adaptive neuro-fuzzy inference systems (ANFISs) and high-order particle filtering. The ANFIS is trained via machine historical failure data. The trained ANFIS and its modeling noise constitute an mth-order hidden Markov model to describe the fault propagation process. The high-order particle filter uses this Markov model to predict the time evolution of the fault indicator in the form of a probability density function. An online update scheme is developed to adapt the Markov model to various machine dynamics quickly. The performance of the proposed method is evaluated by using the testing data from a cracked carrier plate and a faulty bearing. Results show that it outperforms classical condition predictors.read more
Citations
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Journal ArticleDOI
Machinery health prognostics: A systematic review from data acquisition to RUL prediction
TL;DR: A review on machinery prognostics following its whole program, i.e., from data acquisition to RUL prediction, which provides discussions on current situation, upcoming challenges as well as possible future trends for researchers in this field.
Journal ArticleDOI
Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics
TL;DR: A multiobjective deep belief networks ensemble (MODBNE) method that employs a multiobjectives evolutionary algorithm integrated with the traditional DBN training technique to evolve multiple DBNs simultaneously subject to accuracy and diversity as two conflicting objectives is proposed.
Journal ArticleDOI
From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis
Xuewu Dai,Zhiwei Gao +1 more
TL;DR: An outlook to the possible evolution of FDD in industrial automation, including the hybrid FDD and the emerging networked FDD, are presented to reveal the future development direction in this field.
Journal ArticleDOI
An Improved Exponential Model for Predicting Remaining Useful Life of Rolling Element Bearings
TL;DR: The results show that the improved model is able to select an appropriate FPT and reduce random errors of the stochastic process and performs better in the RUL prediction of rolling element bearings than the original exponential model.
Journal ArticleDOI
Review of Hybrid Prognostics Approaches for Remaining Useful Life Prediction of Engineered Systems, and an Application to Battery Life Prediction
Linxia Liao,Felix Köttig +1 more
TL;DR: The review part of this paper specifically focused on the development of hybrid prognostics approaches, attempting to leverage the advantages of combining the progNostics models in the aforementioned different categories for RUL prediction.
References
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Journal ArticleDOI
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
TL;DR: Both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters are reviewed.
Journal ArticleDOI
On sequential Monte Carlo sampling methods for Bayesian filtering
TL;DR: An overview of methods for sequential simulation from posterior distributions for discrete time dynamic models that are typically nonlinear and non-Gaussian, and how to incorporate local linearisation methods similar to those which have previously been employed in the deterministic filtering literature are shown.
Journal ArticleDOI
A review on machinery diagnostics and prognostics implementing condition-based maintenance
TL;DR: This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing CBM with emphasis on models, algorithms and technologies for data processing and maintenance decision-making.
Book
Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions
TL;DR: This chapter discusses Type-2 Fuzzy Sets, a New Direction for FLSs, and Relations and Compositions on different Product Spaces on Different Product Spaces, as well as operations on and Properties of Type-1 Non-Singleton Type- 2 FuzzY Sets.
Book
Intelligent Fault Diagnosis and Prognosis for Engineering Systems
TL;DR: The author examines the development of the Diagnostic Framework for Electrical/Electronic Systems and its applications in CBM/PHM systems, as well as some of the techniques used in model-Based Reasoning and other methods for Fault Diagnosis.