AI Models and Methods in Automotive Manufacturing: A Systematic Literature Review

Mueller, Christoph; Mezhuyev, Vitaliy

doi:10.1007/978-3-031-14748-7_1

Cited by 6 publications

(3 citation statements)

References 71 publications

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“…The development in artificial intelligence and especially ML methods is fast-moving and makes enormous progress every year [33][34][35][36][37]. While in some industrial areas, ML concepts are used productively, in others implementation is lagging.…”

Section: Discussionmentioning

confidence: 99%

Approach to provide interpretability in machine learning models for image classification

Stadlhofer,

Mezhuyev

2023

Industrial Artificial Intelligence

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One of the main reasons why machine learning (ML) methods are not yet widely used in productive business processes is the lack of confidence in the results of an ML model. To improve the situation, interpretability methods may be used, which provide insight into the internal structure of an ML model, and criteria, based on which the model makes a certain prediction. This paper aims to consider the state of the art in interpretability methods and apply the selected methods to an industrial use case. Two methods, called LIME and SHAP, were selected from the literature and next implemented in the use case for image classification using a convolutional neural network. The research methodology consists of three parts, the first is the literature analysis, followed by the practical implementation of an ML model for image classification and the subsequent application of the interpretability methods, and the third part is a multi-criteria comparison of selected LIME and SHAP methods. This work enables companies to select the most effective interpretability method according to their use case and also to increase companies’ motivation for using ML.

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Section: Discussionmentioning

confidence: 99%

Approach to provide interpretability in machine learning models for image classification

Stadlhofer,

Mezhuyev

2023

Industrial Artificial Intelligence

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“…There are several Systematic Literature Reviews (SLR) devoted to the applications of data-driven approaches, Artificial Intelligence (AI) and specifically Machine Learning (ML) for the railway domain. There are reviews of public datasets for railway applications [48], industry 4.0 technologies, applied to the rail transportation industry [49], resilience in transportation systems [50], the effectiveness of safety management systems in transport [51], adoption of ML for failure prediction in industrial maintenance [52] and more general, SLR for AI applications in railway systems [53], SLR on AI models and methods in automotive manufacturing [54]. Two specific surveys on Data-Driven Predictive Maintenance for the Railway Industry [55] and Data-Driven Models for Predictive Maintenance of Railway Tracks [56] do not consider specific railway assets, components, predicted defects, and maintenance conditions, which are critical for the safety and availability of railway operations.…”

Section: Introductionmentioning

confidence: 99%

Predictive Maintenance for Railway Domain: A Systematic Literature Review

Mario

Mezhuyev

Tschandl

2023

IEEE Eng. Manag. Rev.

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Railways are considered to be an environmentally friendly and efficient means of transport for people and goods with increasing importance in the transport policies of many countries. However, the infrastructure and the substantial demand for maintenance create additional costs for railway operators. To overcome outdated maintenance modes, implementation of new solutions, optimization of maintenance activities and resource utilization are required. Through a systematic literature review (SLR), this work evaluates new approaches toward implementing predictive maintenance in the railway domain. A comprehensive search, including the IEEE Xplore, Science Direct, and ACM Digital Library has been conducted, focusing on papers related to predictive maintenance and railway systems, published in peerreviewed journals since 2016. The selected papers were analysed and grouped to allocate the research purposes as well as the considered assets, components, predicted defects, and maintenance conditions. Furthermore, the utilized predictive maintenance algorithms and their limitations are structured and evaluated. Analysis shows that a great variety of algorithms were used for either defect detection or the prediction of conditions of 20 different components, which are critical for the safety and availability of railway operations. The study shows that the proposed approaches were successfully tested and yielded great potential for predictive maintenance solutions. Researchers state to enhance proposed solutions within their future work, increasing accuracy, performance, and widening the area of application in the railway domain. MANAGERIAL RELEVANCE STATEMENTThe paper discusses new predictive maintenance solutions, which have significant potential for saving costs through optimization of resource consumption, reducing the labour intensiveness of maintenance activities, and decreasing effortful tasks such as inspections and patrolling. Implementing the suggested solutions will improve the availability of the railway network, reduce costs related to train interruptions, and simultaneously improve customer satisfaction. Increasing the safety of the railway network through early detection of defects reduces the risk of component-related accidents, as well as the risk of human error, for example leaving defects undetected during long and tiring inspections or patrolling tasks. In addition, workplace health and safety will improve as being present on-site becomes less of a necessity, hence lowering the risk of accidents occurring during maintenance works.

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“…The progress in artificial intelligence (AI) technology has led to a significant focus on the utilization of AI for vehicle operations in the automotive industry [1,2]. In-vehicle dialog systems have transitioned from single-turn interactions to multi-turn dialogues, enabling the capacity for contextualized responses.…”

Section: Introductionmentioning

confidence: 99%

Enhancing In-vehicle Multi-turn Dialogue Text Rewriting with Lightweight Text Filtering

Xue,

Tan,

Lin

et al. 2023

Preprint

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Deep learning has advanced intelligent vehicle development, improving communication and language comprehension. The Transformer model is currently the mainstream approach for text rewriting(coreference resolution) in vehicle dialogue systems, aiming to detect and rephrase sentences that require modification. Nonetheless, the Transformer model, consisting of a stack of Transformer blocks, is complex and extensive, making it inefficient for processing tasks. Accordingly, this paper proposes a proficient approach, denoted as FM-Transformer, for text rewriting of multi-round in-vehicle dialogues. The improvement idea is to add a lightweight text filtering module before the Transformer model, which can quickly classify whether the text of vehicle dialogues needs to be written or not, and only send the sentences that need to be modified into the Transformer model. After analysis, we adopt Fasttext as the lightweight text filtering module. Before and after the improvement of the model, we carried out a control experiment to verify the effectiveness of the FM converter by using a multi-turn vehicle voice conversation data set. The results showed positive results in terms of accuracy and time efficiency, highlighting the potential of FM converters to generate higher quality and more efficient response text.

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AI Models and Methods in Automotive Manufacturing: A Systematic Literature Review

Cited by 6 publications

References 71 publications

Approach to provide interpretability in machine learning models for image classification

Approach to provide interpretability in machine learning models for image classification

Predictive Maintenance for Railway Domain: A Systematic Literature Review

Enhancing In-vehicle Multi-turn Dialogue Text Rewriting with Lightweight Text Filtering

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