A model of the environmentally affected growth of fatigue cracks

Bolotin, V. V.; Shipkov, A. A.

doi:10.1016/s0021-8928(98)00037-9

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…For exemplary purposes, one first-principle model-based approach, within a filtering framework, and one data-driven approach are here illustrated with reference to their application in a prognostic task regarding a non linear fatigue crack growth process, typical of a certain class of industrial and structural equipment (Oswald & Schueller, 1984;Sobezyk & Spencer, 1992;Bolotin & Shipkov, 1998;Myotyri et al, 2006). The choice of the approaches presented is not motivated by any declaration of alleged superiority in comparison to the many other methods proposed in the literature, but by the need to rely on the experience of the author in their development and application.…”

Section: Examplesmentioning

confidence: 99%

Prognostics and Health Management of Industrial Equipment

Zio

2013

Diagnostics and Prognostics of Engineering Systems

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ISBN13: 9781466620957Prognostics and health management (PHM) is a field of research and application which aims at making use of past, present and future information on the environmental, operational and usage conditions of an equipment in order to detect its degradation, diagnose its faults, predict and proactively manage its failures. The present paper reviews the state of knowledge on the methods for PHM, placing these in context with the different information and data which may be available for performing the task and identifying the current challenges and open issues which must be addressed for achieving reliable deployment in practice. The focus is predominantly on the prognostic part of PHM, which addresses the prediction of equipment failure occurrence and associated residual useful life (RUL)

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Section: Examplesmentioning

confidence: 99%

Prognostics and Health Management of Industrial Equipment

Zio

2013

Diagnostics and Prognostics of Engineering Systems

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“…In industries such as nuclear, oil and gas, automotive and chemical, equipments are subjected to several causes of performance degradation and exposed to faulty conditions, e.g., presence of manufacturing defects, unexpected interactions with the environment, wear and tear (Bolotin & Shipkov, 1998;Muller, Suhner, & Iung, 2008;Baraldi, Di Maio, & Zio, 2012;Baraldi, Di Maio, & Zio, 2013c). Capturing the different operational conditions of these equipments, detecting the onset of abnormal conditions and classifying them in different types can aid the decision maker to decide a proper maintenance intervention policy and, hence, increase equipment reliability and system safety while reducing overall corrective maintenance costs (Jardine, Lin, & Banjevic, 2006; Al-Dahidi, Baraldi, Di Maio, & Zio, 2014).…”

Section: Introductionmentioning

confidence: 99%

A Novel Ensemble Clustering for Operational Transients Classification with Application to a Nuclear Power Plant Turbine

Al‐Dahidi

Maio

Baraldi

et al. 2020

IJPHM

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The objective of the present work is to develop a novel approach for combining in an ensemble multiple base clusterings of operational transients of industrial equipment, when the number of clusters in the final consensus clustering is unknown. A measure of pairwise similarity is used to quantify the co-association matrix that describes the similarity among the different base clusterings. Then, a Spectral Clustering technique of literature, embedding the unsupervised K-Means algorithm, is applied to the coassociation matrix for finding the optimum number of clusters of the final consensus clustering, based on Silhouette validity index calculation. The proposed approach is developed with reference to an artificial casestudy, properly designed to mimic the signal trend behavior of a Nuclear Power Plant (NPP) turbine during shut-down. The results of the artificial case have been compared with those achieved by a state-of-art approach, known as Clusterbased Similarity Partitioning and Serial Graph Partitioning and Fill-reducing Matrix Ordering Algorithms (CSPAMETIS). The comparison shows that the proposed approach is able to identify a final consensus clustering that classifies the transients with better accuracy and robustness compared to the CSPA-METIS approach. The approach is, then, validated on an industrial case concerning 149 shut-down transients of a NPP turbine.

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“…Examples of directly measurable degradation indicators are the length of a crack in a structure [44,8,55,40], the light output from fluorescent light bulbs or the thickness of a car tyre tread [15]. Measurement noise can affect the raw data obtained from the sensors, possibly obscuring the signal trend; for this reason, filtering techniques are applied to smooth the HI.…”

Section: Introductionmentioning

confidence: 99%

Differential evolution-based multi-objective optimization for the definition of a health indicator for fault diagnostics and prognostics

Baraldi

Bonfanti

Zio

2018

Mechanical Systems and Signal Processing

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The identification of the current degradation state of an industrial component and the prediction of its future evolution is a fundamental step for the development of condition-based and predictive maintenance approaches. The objective of the present work is to propose a general method for extracting a health indicator to measure the amount of component degradation from a set of signals measured during operation. The proposed method is based on the combined use of feature extraction techniques, such as Empirical Mode Decomposition and Auto-Associative Kernel Regression, and a multi-objective Binary Differential Evolution (BDE) algorithm for selecting the subset of features optimal for the definition of the health indicator. The objectives of the optimization are desired characteristics of the health indicator, such as monotonicity, trendability and prognosability. A case study is considered, concerning the prediction of the remaining useful life of turbofan engines. The obtained results confirm that the method is capable of extracting health indicators suitable for accurate prognostics.

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A model of the environmentally affected growth of fatigue cracks

Cited by 5 publications

References 8 publications

Prognostics and Health Management of Industrial Equipment

Prognostics and Health Management of Industrial Equipment

A Novel Ensemble Clustering for Operational Transients Classification with Application to a Nuclear Power Plant Turbine

Differential evolution-based multi-objective optimization for the definition of a health indicator for fault diagnostics and prognostics

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