Machine learning for reliability engineering and safety applications: Review of current status and future opportunities

Xu, Zhaoyi; Saleh, Joseph H.

doi:10.1016/j.ress.2021.107530

Cited by 280 publications

(76 citation statements)

References 128 publications

(138 reference statements)

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“…Following the overview provided by Xu and Saleh [26] on ML methods, based on their capabilities and features, the ML adopted in EsOpIA has the characteristics of a supervised learning, since the aim for the system is to learn a target function that can be adopted to predict the values of a class. The annotation process, as described in [28], for training EsOpIA model required about fifteen iterations, with sets of documents ranging from 5 to 10 in the starting phase, up to 20 and 50 in the most advanced stages of learning.…”

Section: Performance Of the Machine-learning Modelmentioning

confidence: 99%

See 1 more Smart Citation

Near Miss Archive: a Challenge to Share Knowledge Among Inspectors and Improve Seveso Inspections

Ansaldi¹,

Agnello²,

Pirone³

et al. 2021

Preprint

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In European Seveso Legislation for the control of the hazard of major accidents (Directive 2015/12/UE), the Safety Management System SMS is an essential obligation for managers and the authorities are required to periodically verify its adequateness through periodical inspections at Seveso sites. One of the pillars of the SMS is the collection and analysis of documents on accidents, near misses and possibly anomalies, in order to identify weaknesses and implement continuous improvement. In Italy, for a few years, the documents, gathered from all Italian Seveso sites by the inspectors, have been archived and used for research purposes. The archive currently contains some 4000 reports, collected in five years by some 100 inspectors throughout Italy. The paper discusses in the detail the challenges faced to extract the knowledge hidden in the documents and make it usable through the design of a robust model. For this aim, Machine Learning techniques have been used as a preprocessing of the reports for extracting the concepts and their relations, organized into an entity-relation model. The effectiveness of this methodology and its potentiality are pointed out by investigating a few hot topics, exploiting the information contained in the repository.

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Section: Performance Of the Machine-learning Modelmentioning

confidence: 99%

“…Xu and Saleh [26] provide a detailed overview of different ML categories and corresponding models and algorithms used, reviewing ML applications in reliability and safety applications. They also give a rough definition of ML, as a data analysis method that iteratively learns from past data and adapts independently when applied to new data.…”

Section: Introductionmentioning

confidence: 99%

Near Miss Archive: a Challenge to Share Knowledge Among Inspectors and Improve Seveso Inspections

Ansaldi¹,

Agnello²,

Pirone³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Nowadays, machine-learning algorithms are widely implemented in the production environment (Zhang, Wang, and Gao 2019). The application of machine learning is encouraged by industrial communities due to its additional capabilities to save on resources, machining time and energy, and increase yield in areas where traditional methods such as six sigma strategies have reached their limits (Köksal, Batmaz, and Testik 2011;Golkarnareji et al, 2019;Ren 2021;Xu et al, 2021).…”

Section: Digitalization Of Industries and Machine Learningmentioning

confidence: 99%

Application of machine learning algorithm in the sheet metal industry: an exploratory case study

Shamsuzzoha

Kankaanpaa

Nguyen

et al. 2021

International Journal of Computer Integrated Manufacturing

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This study solved a practical problem in a case in the sheet metal industry using machine learning and deep learning algorithms. The problem in the case company was related to detecting the minimum gaps between components, which were produced after the punching operation of a metal sheet. Due to the narrow gaps between the components, an automated sheer machine could not grip the rest of the sheet skeleton properly after the punching operation. This resulted in some of the scraped sheet on the worktable being left behind, which needed a human operator to intervene. This caused an extra trigger to the production line that resulted in a break in production. To solve this critical problem, the relevant images of the components and the gaps between them were analyzed using machine learning and deep learning techniques. The outcome of this study contributed to eliminating the production bottleneck by optimizing the gaps between the punched components. This optimization process facilitated the easy and safe movement of the gripper machine and contributed to minimizing the sheet waste.

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“…UQ is getting vast popularity due to its demand in ML and DL methods [6][7][8][9] . In most of the previous studies, researchers in UQ are splitting the dataset randomly 10,11 . The performance of the trained NN varies based on the data splitting 12,13 .…”

Section: Background and Summarymentioning

confidence: 99%

Synthetic Datasets for Numeric Uncertainty Quantification

Kabir

Abdar

Khosravi

et al. 2021

Preprint

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In this paper, we propose ten synthetic datasets for point prediction and numeric uncertainty quantification (UQ). These datasets are split into train, validation, and test sets for model benchmarking. Equations and the description of each dataset are provided in detail. We also present representative shallow neural network (NN) training and Random Vector Functional Link (RVFL) training examples. Both training train models for the point prediction. We perform uncertainty quantification with the consideration of a gaussian and homoscedastic distribution. As the distribution consideration and models are rudimental, much room exists for further explorations and improvements. The dataset and scripts are available at the following link: https://github.com/dipuk0506/UQ-Data

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Machine learning for reliability engineering and safety applications: Review of current status and future opportunities

Cited by 280 publications

References 128 publications

Near Miss Archive: a Challenge to Share Knowledge Among Inspectors and Improve Seveso Inspections

Near Miss Archive: a Challenge to Share Knowledge Among Inspectors and Improve Seveso Inspections

Application of machine learning algorithm in the sheet metal industry: an exploratory case study

Synthetic Datasets for Numeric Uncertainty Quantification

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