Topic
Condition monitoring
About: Condition monitoring is a research topic. Over the lifetime, 13911 publications have been published within this topic receiving 201649 citations.
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TL;DR: An integrated systemic model for the integration of human reliability model with condition based maintenance (CBM) optimization and an exact simulation-optimization algorithm based on the use of two joint Fibonacci algorithms is proposed for global optimization of CM scheduling with human error.
68 citations
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TL;DR: A bearing fault detection scheme based on support vector machine as a classification method and binary particle swarm optimization algorithm (BPSO) based on maximal class separability as a feature selection method based on regularized Fisher's criterion are proposed.
Abstract: Condition monitoring of rotating machinery has attracted more and more attention in recent years in order to reduce the unnecessary breakdowns of components such as bearings and gears which suffer frequently from failures. Vibration based approaches are the most commonly used techniques to the condition monitoring tasks. In this paper, we propose a bearing fault detection scheme based on support vector machine as a classification method and binary particle swarm optimization algorithm (BPSO) based on maximal class separability as a feature selection method. In order to maximize the class separability, regularized Fisher's criterion is used as a fitness function in the proposed BPSO algorithm. This approach was evaluated using vibration data of bearing in healthy and faulty conditions. The experimental results demonstrate the effectiveness of the proposed method.
68 citations
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TL;DR: In this study, statistical features were extracted from vibration signals, feature selection was carried out using a J48 decision tree algorithm, feature classification was performed using best-first tree algorithm and functional trees algorithm and the better algorithm is suggested for fault diagnosis of wind turbine blade.
Abstract: Wind energy is one of the important renewable energy resources available in nature. It is one of the major resources for production of energy because of its dependability due to the development of the technology and relatively low cost. Wind energy is converted into electrical energy using rotating blades. Due to environmental conditions and large structure, the blades are subjected to various vibration forces that may cause damage to the blades. This leads to a liability in energy production and turbine shutdown. The downtime can be reduced when the blades are diagnosed continuously using structural health condition monitoring. These are considered as a pattern recognition problem which consists of three phases namely, feature extraction, feature selection, and feature classification. In this study, statistical features were extracted from vibration signals, feature selection was carried out using a J48 decision tree algorithm and feature classification was performed using best-first tree algorithm and functional trees algorithm. The better algorithm is suggested for fault diagnosis of wind turbine blade.
68 citations
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TL;DR: In this article, a simplified mathematical model was developed and a series of experiments were carried out on a roller rig for the detection of wheel flats and rail surface defects using three commonly used time-frequency analysis techniques: Short-Time Fourier Transform, Wigner-Ville transform and wavelet transform.
Abstract: Damage to the surface of railway wheels and rails commonly occurs in most railways. If not detected, it can result in the rapid deterioration and possible failure of rolling stock and infrastructure components causing higher maintenance costs. This paper presents an investigation into the modelling and simulation of wheel-flat and rail surface defects. A simplified mathematical model was developed and a series of experiments were carried out on a roller rig. The time–frequency analysis is a useful tool for identifying the content of a signal in the frequency domain without losing information about its time domain characteristics. Because of this, it is widely used for dynamic system analysis and condition monitoring and has been used in this paper for the detection of wheel flats and rail surface defects. Three commonly used time–frequency analysis techniques: Short-Time Fourier Transform, Wigner–Ville transform and wavelet transform were investigated in this work.
68 citations
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TL;DR: In this article, the authors present a model directed to the determination of the ordering decision for a spare part when the component in operation is subject to a condition monitoring program, based on the remaining useful life (RUL) estimation obtained through the assessment of component age and condition indicators.
68 citations