Typical short-term remedy knowledge mining for product quality problem-solving based on bipartite graph clustering

Xu, Zhaoguang; Dang, Yanzhong; Zhang, Zhongzhao; Chen, Jingfeng

doi:10.1016/j.compind.2020.103277

Cited by 7 publications

(2 citation statements)

References 43 publications

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“…They use the ontology to process (e.g., identify n-grams), structure and represent new text data in various forms (2018, p. 214) to facilitate the design and managerial decisions. Xu et al (2020) obtain the text data of 1844 problems and 1927 short-term remedies from a vehicle manufacturer. To link the problems and remedies, they transform the text using term frequency – inverse document frequency (TD-IDF) and perform K-means clustering for problems and short-term remedies, while also linking the clusters.…”

Section: Reviewmentioning

confidence: 99%

Natural language processing in-and-for design research

2022

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We review the scholarly contributions that utilise natural language processing (NLP) techniques to support the design process. Using a heuristic approach, we gathered 223 articles that are published in 32 journals within the period 1991–present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions and others. Upon summarising and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research.

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Section: Reviewmentioning

confidence: 99%

Natural language processing in-and-for design research

2022

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“…Clustering methods are tools used on many research fronts, including data mining so that significant knowledge is extracted from apparently unstructured samples [23][24][25][26]. In addition, clustering is a way to group data and identify patterns coherently and unsupervised [27,28]. Among the different techniques, the literature points out those computational intelligence techniques are an alternative to traditional methods such as K-Means in real problems.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection in Automotive Industry Using Clustering Methods—A Case Study

Guerreiro

Barchi

et al. 2021

Applied Sciences

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In automotive industries, pricing anomalies may occur for components of different products, despite their similar physical characteristics, which raises the total production cost of the company. However, detecting such discrepancies is often neglected since it is necessary to find the problems considering the observation of thousands of pieces, which often present inconsistencies when specified by the product engineering team. In this investigation, we propose a solution for a real case study. We use as strategy a set of clustering algorithms to group components by similarity: K-Means, K-Medoids, Fuzzy C-Means (FCM), Hierarchical, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Self-Organizing Maps (SOM), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE). We observed that the methods could automatically perform the grouping of parts considering physical characteristics present in the material master data, allowing anomaly detection and identification, which can consequently lead to cost reduction. The computational results indicate that the Hierarchical approach presented the best performance on 1 of 6 evaluation metrics and was the second place on four others indexes, considering the Borda count method. The K-Medoids win for most metrics, but it was the second best positioned due to its bad performance regarding SI-index. By the end, this proposal allowed identify mistakes in the specification and pricing of some items in the company.

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A task‐centric knowledge graph construction method based on multi‐modal representation learning for industrial maintenance automation

Liu,

2024

Engineering Reports

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Maintenance manuals are crucial information sources for maintenance and repair. Prior studies explored factual knowledge extraction from textual documents. However, maintenance knowledge in manuals is more task‐centric rather than factual knowledge and often documented in an unstructured Portable Document Format (PDF), posing challenges for knowledge extraction. Addressing this, this research develops effective methods to extract task‐centric maintenance knowledge from unstructured PDF manuals. A new Task‐centric Knowledge Graph (TCKG) schema centralized on maintenance task components (MTCs) is proposed to address the need for structured knowledge representation. A method (Heterogeneous Graph‐based Method, HGM) for knowledge extraction is then proposed, which is enhanced by incorporating visual and spatial information. In the experiments, the proposed HGM exhibits robust performance in the knowledge extraction process, surpassing the baseline Graph‐based Interaction Model with a Tracker (GIT) method in MTCs extraction by 13.3%, and the baseline Translate Embedding (TransE) method in MTCs' relation extraction by 3.8%. A series of ablation studies also prove that including visual and spatial information through the proposed method can improve the relation extraction performance by over 10%. This research supplies valuable insights for future developments in information extraction from maintenance manuals.

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Typical short-term remedy knowledge mining for product quality problem-solving based on bipartite graph clustering

Cited by 7 publications

References 43 publications

Natural language processing in-and-for design research

Natural language processing in-and-for design research

Anomaly Detection in Automotive Industry Using Clustering Methods—A Case Study

A task‐centric knowledge graph construction method based on multi‐modal representation learning for industrial maintenance automation

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