Prognostics and Health Management: A Review on Data Driven Approaches

Tsui, Kwok Leung; Chen, Nan; Qiang, Zexuan; Hai, Yizhen; Wang, Wenbin

doi:10.1155/2015/793161

Cited by 288 publications

(152 citation statements)

References 122 publications

(138 reference statements)

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“…Sikorska et al [200] and Ahmadzadeh and Lundberg [201] reviewed the advances in RUL estimation from the industry point of view. Tsui et al [202] provided a concise review of mainstream methods in major aspects of the prognostic and health management framework. The distinction between a damage detection model and a damage prognosis model is that the prognosis model needs to have the ability to estimate the damage evolution in the future, while the detection model only infers the current state of damage and do not necessarily have the ability to model progressive damage mechanisms such as crack growth [203].…”

Section: Damage Prognosis Methodologiesmentioning

confidence: 99%

The State-of-the-Art on Framework of Vibration-Based Structural Damage Identification for Decision Making

2017

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Abstract:Research on detecting structural damage at the earliest possible stage has been an interesting topic for decades. Among them, the vibration-based damage detection method as a global technique is especially pervasive. The present study reviewed the state-of-the-art on the framework of vibration-based damage identification in different levels including the prediction of the remaining useful life of structures and the decision making for proper actions. This framework consists of several major parts including the detection of damage occurrence using response-based methods, building reasonable structural models, selecting damage parameters and constructing objective functions with sensitivity analysis, adopting optimization techniques to solve the problem, predicting the remaining useful life of structures, and making decisions for the next actions. For each part, the commonly used methods were reviewed and the merits and drawbacks were summarized to give recommendations. This framework is aimed to guide the researchers and engineers to implement step by step the structure damage identification using vibration measurements. Finally, the future research work in this field is recommended.

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Section: Damage Prognosis Methodologiesmentioning

confidence: 99%

The State-of-the-Art on Framework of Vibration-Based Structural Damage Identification for Decision Making

2017

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“…The defining feature of a problem space apt for the application of ML approaches is data availability: the increased existence of historical reliability data regarding rotating plant, engines, and other key engineering assets has prompted interest in such techniques amongst the more general class of data-driven [8], [9] approaches to reliability engineering.…”

Section: Machine Learning In Reliability Engineeringmentioning

confidence: 99%

Machine Learning Model for Event-Based Prognostics in Gas Circulator Condition Monitoring

2017

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The Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Abstract-Gas circulator (GC) units are an important rotating asset used in the Advanced Gas-cooled Reactor (AGR) design, facilitating the flow of CO2 gas through the reactor core. The ongoing maintenance and examination of these machines is important for operators in order to maintain safe and economic generation. GCs experience a dynamic duty cycle with periods of non-steady state behavior at regular refuelling intervals, posing a unique analysis problem for reliability engineers.In line with the increased data volumes and sophistication of available technologies, the investigation of predictive and prognostic measurements has become a central interest in rotating asset condition monitoring. However, many of the state-of-theart approaches finding success deal with the extrapolation of stationary time series feeds, with little to no consideration of more-complex but expected events in the data.In this paper we demonstrate a novel modelling approach for examining refuelling behaviors in GCs, with a focus on estimating their health state from vibration data. A machine learning model was constructed using the operational history of a unit experiencing an eventual inspection-based failure. This new approach to examining GC condition is shown to correspond well with explicit remaining useful life (RUL) measurements of the case study, improving on the existing rudimentary extrapolation methods often employed in rotating machinery health monitoring.

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“…There are three main parts in the whole cycle of power transformer's PHM, which are fault diagnosis, fault prognostic and condition-based maintenance [8]. The purpose of fault diagnosis is to diagnose and identify the root causes of transformer failure; the root causes can provide useful information for the prognostic models as well as feedback for transformer design improvement.…”

Section: Primary Mission Of Phmmentioning

confidence: 99%

Large Power Transformer Fault Diagnosis and Prognostic Based on DBNC and D-S Evidence Theory

Li¹,

Yu²,

Fan³

et al. 2017

EPE

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Power transformer is a core equipment of power system, which undertakes the important functions of power transmission and transformation, and its safe and stable operation has great significance to the normal operation of the whole power system. Due to the complex structure of the transformer, the use of single information for condition-based maintenance (CBM) has certain limitations, with the help of advanced sensor monitoring and information fusion technology, multi-source information is applied to the prognostic and health management (PHM) of power transformer, which is an important way to realize the CBM of power transformer. This paper presents a method which combine deep belief network classifier (DBNC) and D-S evidence theory, and it is applied to the PHM of the large power transformer. The experimental results show that the proposed method has a high correct rate of fault diagnosis for the power transformer with a large number of multi-source data.

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Prognostics and Health Management: A Review on Data Driven Approaches

Cited by 288 publications

References 122 publications

The State-of-the-Art on Framework of Vibration-Based Structural Damage Identification for Decision Making

The State-of-the-Art on Framework of Vibration-Based Structural Damage Identification for Decision Making

Machine Learning Model for Event-Based Prognostics in Gas Circulator Condition Monitoring

Large Power Transformer Fault Diagnosis and Prognostic Based on DBNC and D-S Evidence Theory

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