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Condition monitoring

About: Condition monitoring is a research topic. Over the lifetime, 13911 publications have been published within this topic receiving 201649 citations.


Papers
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Journal ArticleDOI
TL;DR: In this article, the authors present probabilistic models that employ a variety of methods including discrete-time Markov chains, Monte Carlo methods and time series modelling to evaluate the economic benefits of condition monitoring in offshore wind farms.
Abstract: Condition monitoring (CM) systems are increasingly installed in wind turbines with the goal of providing component-specific information to wind farm operators, theoretically increasing equipment availability via maintenance and operating actions based on this information. In the offshore case, economic benefits of CM systems are often assumed to be substantial, as compared with experience of onshore systems. Quantifying this economic benefit is non-trivial, especially considering the general lack of utility experience with large offshore wind farms. A quantitative measure of these benefits is therefore of value to utilities and operations and maintenance (O & M) groups involved in planning and operating future offshore wind farms. The probabilistic models presented in this paper employ a variety of methods including discrete-time Markov Chains, Monte Carlo methods and time series modelling. The flexibility and insight provided by this framework captures the necessary operational nuances of this complex pr...

139 citations

Journal ArticleDOI
01 Jul 2009
TL;DR: This paper presents a sensory-updated degradation-based predictive maintenance policy that combines component-specific real-time degradation signals, acquired during operation, with degradation and reliability characteristics of the component's population to predict and update the residual life distribution (RLD).
Abstract: This paper presents a sensory-updated degradation-based predictive maintenance policy (herein referred to as the SUDM policy). The proposed maintenance policy utilizes contemporary degradation models that combine component-specific real-time degradation signals, acquired during operation, with degradation and reliability characteristics of the component's population to predict and update the residual life distribution (RLD). By capturing the latest degradation state of the component being monitored, the updating process provides a more accurate of the remaining life. With the aid of a stopping rule, maintenance routines are scheduled based on the most recently updated RLD. The performance of the proposed maintenance policy is evaluated using a simulation model of a simple manufacturing cell. Frequency of unexpected failures and overall maintenance costs are computed and compared with two other benchmark maintenance policies: a reliability-based and a conventional degradation-based maintenance policy (without any sensor-based updating).

139 citations

Journal ArticleDOI
TL;DR: A new data-driven prognostics approach namely, an “enhanced multivariate degradation modeling,” which enables modeling degrading states of machinery without assuming a homogeneous pattern is contributed.
Abstract: Prognostics is a core process of prognostics and health management (PHM) discipline, that estimates the remaining useful life (RUL) of a degrading machinery to optimize its service delivery potential. However, machinery operates in a dynamic environment and the acquired condition monitoring data are usually noisy and subject to a high level of uncertainty/unpredictability, which complicates prognostics. The complexity further increases, when there is absence of prior knowledge about ground truth (or failure definition). For such issues, data-driven prognostics can be a valuable solution without deep understanding of system physics. This paper contributes a new data-driven prognostics approach namely, an “enhanced multivariate degradation modeling,” which enables modeling degrading states of machinery without assuming a homogeneous pattern. In brief, a predictability scheme is introduced to reduce the dimensionality of the data. Following that, the proposed prognostics model is achieved by integrating two new algorithms namely, the summation wavelet-extreme learning machine and subtractive-maximum entropy fuzzy clustering to show evolution of machine degradation by simultaneous predictions and discrete state estimation. The prognostics model is equipped with a dynamic failure threshold assignment procedure to estimate RUL in a realistic manner. To validate the proposition, a case study is performed on turbofan engines data from PHM challenge 2008 (NASA), and results are compared with recent publications.

139 citations

Proceedings ArticleDOI
12 Oct 2003
TL;DR: In this paper, the experimental investigation for incipient fault detection and fault detection methods existing in the literature suitably adapted for use in wind generator systems using doubly fed induction generators (DFIGs).
Abstract: This paper focuses on the experimental investigation for incipient fault detection and fault detection methods existing in the literature suitably adapted for use in wind generator systems using doubly fed induction generators (DFIGs) Three main experiments (one for stator phase unbalance, one for rotor phase unbalance and one for turn-to-turn faults) have been performed to study the electrical behaviour of the DFIG The article aims to provide wind generators with further documentation for an advanced condition monitoring system, in order to avoid undesirable operating conditions and to detect and diagnose incipient electrical faults

139 citations

Journal ArticleDOI
TL;DR: In this paper, a modified and effective signal processing algorithm is designed to diagnose localized defects on rolling element bearings components under different operating speeds, loadings, and defect sizes, which is based on optimizing the ratio of Kurtosis and Shannon entropy.

139 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023164
2022413
2021798
2020927
2019936
2018906