Interpretable Machine Learning: A brief survey from the predictive maintenance perspective

Vollert, Simon; Atzmueller, Martin; Theissler, Andreas

doi:10.1109/etfa45728.2021.9613467

Cited by 59 publications

(28 citation statements)

References 81 publications

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“…Besides these challenges, characteristics, tools, solutions, adoption, and recommendations in these papers as shown in the systematic map in Figure 4, interpretable and Explainable machine learning models are deficient [65], [66], which is remarkable in SME scenarios. For example, [67] proposed Explainable Artificial Intelligence (XAI) in remaining useful life prediction of Turbofan Engines.…”

Section: Best Practices (Rq4)mentioning

confidence: 97%

A Systematic Mapping Study of Predictive Maintenance in SMEs

et al. 2022

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The rapid growth of Industry 4.0 and predictive methods fostered a great potential for state-of-the-art techniques in the industrial sector, especially in smart factories. The equipment failure or system breakdowns during run time of a factory creates a severe problems towards impoverishment of the production system and destitution of the business. Predictive Maintenance (PdM) is a cost-saving and data driven technique to predict the maintenance time of in-service equipment or systems to reduce breakdown time and increase productivity. Although PdM is pragmatically adopted in large-scale industries, there is a lack of studies that map the PdM adoption in small and medium-sized enterprises (SMEs). In this systematic mapping study (SMS), we focus on predictive maintenance from an SME perspective to explore the field for researchers, scientists, and developers to comprehend the potential of PdM systems, their challenges, distinctive characteristics, and best practices in SMEs. Our study is based on four research questions comprised of demographic data, key challenges, distinctive characteristics, and best practices of predictive maintenance in SMEs. We found that the current literature on PdM is deficient in the SME domain, especially the financial side is vague. There is a huge potential for PdM in SMEs to design cost models and focus on data availability impediments. Management and monitoring of PdM and skilled personnel are also inadequate. Thus, we present a study that extracts the knowledge from the existing literature about PdM in SMEs, finds the research gap, and can assist in identifying the barriers and challenges of PdM adoption in SMEs.

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Section: Best Practices (Rq4)mentioning

confidence: 97%

A Systematic Mapping Study of Predictive Maintenance in SMEs

et al. 2022

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“…However, while these surveys are generalists, we focus on explainers for time series classification problems. We underline that some surveys related to XAI are focused not only on machine learning but also on social studies [22], [23], recommendation systems [24], model-agents [25], and domain-specific applications such as health and medicine [26] or predictive maintenance [27].…”

Section: Related Workmentioning

confidence: 99%

“…d: Easy-to-use explainers are desirable Some XAI methods to explain black-box models might be viewed as black-boxes themselves, as pointed out in previous works [27]. Explaining complex ML models calls for sophisticated and often complicated XAI methods.…”

Section: C: Domain-specific Explanations For Specific Applicationsmentioning

confidence: 99%

Explainable AI for Time Series Classification: A Review, Taxonomy and Research Directions

et al. 2022

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Time series data is increasingly used in a wide range of fields, and it is often relied on in crucial applications and high-stakes decision-making. For instance, sensors generate time series data to recognize different types of anomalies through automatic decision-making systems. Typically, these systems are realized with machine learning models that achieve top-tier performance on time series classification tasks. Unfortunately, the logic behind their prediction is opaque and hard to understand from a human standpoint. Recently, we observed a consistent increase in the development of explanation methods for time series classification justifying the need to structure and review the field. In this work, we (a) present the first extensive literature review on Explainable AI (XAI) for time series classification, (b) categorize the research field through a taxonomy subdividing the methods into time points-based, subsequences-based and instance-based, and (c) identify open research directions research directions regarding the type of explanations and the evaluation of explanations and interpretability.

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“…Posthoc explanations require that a low-dimensional and to some extent local representation of the learned behavior can be created without too much loss of information, which can for example be visually perceived. Thereby, significant research gaps in PHM on interpretable machine learning especially on models of time series analysis and prediction applications in general exist (Vollert et al, 2021).…”

Section: Concepts For the Integration Of Cross-application Knowledge ...mentioning

confidence: 99%

On the Integration of Fundamental Knowledge about Degradation Processes into Data-Driven Diagnostics and Prognostics Using Theory-Guided Data Science

Hagmeyer

Zeiler²,

Huber³

2022

PHME_CONF

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In Prognostics and Health Management, there are three main approaches for implementing diagnostic and prognostic applications. These approaches are data-driven methods, physical model-based methods, and combinations of them, in the form of hybrid methods. Each of them has specific advantages but also limitations for their purposeful implementation. In the case of data-driven methods, one of the main limitations is the availability of sufficient training data that adequately cover the relevant state space. For model-based methods, on the other hand, it is often the case that the degradation process of the considered technical system is of significant complexity. In such a scenario physics-based modeling requires great effort or is not possible at all. Combinations of data-driven and model-based approaches in form of hybrid approaches offer the possibility to partially mitigate the shortcomings of the other two approaches, however, require a sufficiently detailed data-driven and physics-based model. This paper addresses the transitional field between data-driven and hybrid approaches. Despite the issues of formulating a physics-based model that provides a representation of the degradation process, basic knowledge of the considered system and of the laws governing its degradation process is usually available. Integration of such knowledge into a machine learning process is part of a research field that is either called theory-guided data science, (physics) informed machine learning, physics-based learning or physics guided machine learning. First, the state of research in Prognostics and Health Management on methods of this field is presented and existing research gaps are outlined. Then, a concept is introduced for incorporating fundamental knowledge, such as monotonicity constraints, into data-driven diagnostic and prognostic applications using approaches from theory-guided data science. A special aspect of this concept is its cross-application usability through the consideration of knowledge that repeatedly occurs in diagnostics and prognostics. This is, for example, knowledge about physically justified boundaries whose compliance makes a prediction of the data-driven model plausible in the first place.

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Interpretable Machine Learning: A brief survey from the predictive maintenance perspective

Cited by 59 publications

References 81 publications

A Systematic Mapping Study of Predictive Maintenance in SMEs

A Systematic Mapping Study of Predictive Maintenance in SMEs

Explainable AI for Time Series Classification: A Review, Taxonomy and Research Directions

On the Integration of Fundamental Knowledge about Degradation Processes into Data-Driven Diagnostics and Prognostics Using Theory-Guided Data Science

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