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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.


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Journal ArticleDOI
04 Jan 2018-Energies
TL;DR: A new cloud-based condition monitoring and fault diagnosis platform for the large-scale Li-ion BESSs that incorporates the Internet of Things embedded in the battery modules and the cloud battery management platform is proposed.
Abstract: Performance of the current battery management systems is limited by the on-board embedded systems as the number of battery cells increases in the large-scale lithium-ion (Li-ion) battery energy storage systems (BESSs). Moreover, an expensive supervisory control and data acquisition system is still required for maintenance of the large-scale BESSs. This paper proposes a new cloud-based battery condition monitoring and fault diagnosis platform for the large-scale Li-ion BESSs. The proposed cyber-physical platform incorporates the Internet of Things embedded in the battery modules and the cloud battery management platform. Multithreads of a condition monitoring algorithm and an outlier mining-based battery fault diagnosis algorithm are built in the cloud battery management platform (CBMP). The proposed cloud-based condition monitoring and fault diagnosis platform is validated by using a cyber-physical testbed and a computational cost analysis for the CBMP. Therefore, the proposed platform will support the on-board health monitoring and provide an intelligent and cost-effective maintenance of the large-scale Li-ion BESSs.

69 citations

Proceedings ArticleDOI
07 May 2007
TL;DR: In this paper, the authors proposed a low cost method to detect the changes in equivalent series resistor (ESR) and the capacitance value of an electrolytic capacitor in order to realize the real-time condition monitoring of an ECS.
Abstract: The objective of this paper is to propose a new low cost method to detect the changes in equivalent series resistor (ESR) and the capacitance value of an electrolytic capacitor in order to realize the real-time condition monitoring of an electrolytic capacitor. Experimental and simulation results are discussed to illustrate the proposed condition monitoring technique. In addition, it is shown that the proposed method can be used for a non-stationary system where waveforms are continuously varying in amplitude, frequency, and phase. The proposed on-line failure prediction method has the merits of low cost and circuit simplicity.

69 citations

Journal ArticleDOI
TL;DR: It was observed that with the increase in the number of features in the data set, the accuracy, sensitivity, TPR, TNR, F1 score and Kappa metrics increased above 99% at 95% confidence interval, and FPR and FNR metrics fell below 1%.

69 citations

Patent
07 Nov 1997
TL;DR: In this paper, a real-time electric motor diagnostics and condition monitoring system is presented, which includes a set of sensors, a processing unit, a memory and an output interface for communicating alarms, warnings and calculated operating parameter values or the like to a display device and to an external supervisor having wireless paging capability to alert a remote operator or maintenance personnel.
Abstract: A method and apparatus for real-time electric motor diagnostics and condition monitoring. While the motor is energized, dynamic operating parameters are determined and a notification signed is generated if predetermined criterion are satisfied. The diagnostic apparatus is integrated with the motor and includes a set of sensors, a processing unit, a memory and an output interface for communicating alarms, warnings and calculated operating parameter values or the like to a display device and to an external supervisor having wireless paging capability to alert a remote operator or maintenance personnel. In a normal operating mode, the processing unit calculates a general class of derived motor operating parameters such as over-temperature, over-voltage, over-current, excessive vibration, and phase imbalance. When an abnormal condition is observed, the processing unit modifies certain data acquisition parameters as necessary to effect an alternative data acquisition strategy which would more likely lead to data dispositive of the condition of the motor.

69 citations

Journal ArticleDOI
TL;DR: This work develops a framework for incorporating real-time condition monitoring information into inventory decisions for spare parts and proposes a myopic critical fractile policy that captures the essence of the optimal policy, but is easier to compute.
Abstract: Lack of coordination between machinery fault diagnosis and inventory management for spare parts can lead to increased inventory costs and disruptions in production activity. We develop a framework for incorporating real-time condition monitoring information into inventory decisions for spare parts. We consider a manufacturer who periodically replenishes inventory for a machine part that is subject to deterioration. The deterioration is captured via condition monitoring and modeled using a Wiener process. The resulting degradation model is used to derive the life distribution of a functioning part and to estimate the demand distribution for spare parts. This estimation is periodically updated, in a Bayesian manner, as additional information on part deterioration is obtained. We develop an inventory model that incorporates this updated demand distribution and demonstrate that a dynamic base-stock policy, in which the optimal base-stock level is a function of some subset of the observed condition monitoring information, is optimal. We propose a myopic critical fractile policy that captures the essence of the optimal policy, but is easier to compute. Computational experiments indicate that this heuristic performs quite well relative to the optimal policy. Adaptive inventory policies such as these can help manufacturers to increase machine availability and reduce inventory costs.

69 citations


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