Predictive maintenance on sensorized stamping presses by time series segmentation, anomaly detection, and classification algorithms

Coelho, Daniel M.; Costa, Diogo; Breda, Ana; Almeida, Duarte; Santos, José

doi:10.1016/j.procs.2022.01.318

Cited by 27 publications

(19 citation statements)

References 22 publications

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“…Due to the ease of obtaining such a signal and the low cost of installing the sensor, this method was considered promising. The use of machine learning techniques to control the stamping process is presented in, among others, [ 26 , 27 ]. The method of detecting defects in drawpieces using vision systems is presented in, among others, [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Control System for Automated Technological Process of Hot Stamping—A Case Study

Wróbel

Danielczyk

2023

Materials

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Hot forming process has been used more and more frequently in the production of body structures of modern ultralight passenger cars for several years. This, unlike the commonly used cold stamping, is a complicated process, combining heat treatment and plastic-forming methods. For this reason, permanent control at each stage is required. This includes, among others, measurement of the blank thickness, monitoring its heating process in the suitable atmosphere in the furnace, control of the forming process itself, measurement of shape-dimensional accuracy as well as mechanical parameters of the finished drawpiece. This paper discusses the method of controlling the values of production parameters during the hot stamping process of a selected drawpiece. For this purpose, digital twins of the production line and the stamping process, made in accordance with the assumptions of Industry 4.0, have been used. Individual components of the production line with sensors for monitoring process parameters have been shown. The system’s response to emerging threats has also been described. The correctness of the adopted values is confirmed via tests of mechanical properties and the assessment of the shape-dimensional accuracy of a drawpiece test series.

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Section: Introductionmentioning

confidence: 99%

Control System for Automated Technological Process of Hot Stamping—A Case Study

Wróbel

Danielczyk

2023

Materials

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“…Image classification is an image-level visual recognition task that aims to classify each visual image into one of the pre-defined semantic categories; object detection is an instance-level visual recognition task that locates all the objects in a visual image and recognizes their semantic categories; semantic segmentation is a pixel-level visual recognition task that aims to assign a semantic category label to each and every pixel of an image. The progress in this research field enable a wide range of applications in computer vision, including autonomous vehicles [25][26][27][28][29][30], the analysis of medical images [31][32][33][34][35], the surveillance of manufacturing [37][38][39][40][41][42], construction [43][44][45][46], agriculture [47][48][49][50][51][52] and retail [53][54][55][56], and augmented and virtual reality in entertainment [57][58][59][60]. The technical methods of visual recognition can be broadly…”

Section: Visual Recognitionmentioning

confidence: 99%

“…object detection [21,22] and semantic segmentation [23,24]. In practice, visual recognition (i.e., classification, detection and segmentation) plays a significant role in various computer vision scenarios and applications including transportation (e.g., autonomous vehicles [25,26], drones [27,28] and robots [29,30]), healthcare (e.g., analysis of CT [31] and MRI [32,33] images, cancer detection [34,35] and patient movement analysis [36]), manufacturing (e.g., defect inspection [37,38], scene text recognition [39,40] and product assembly [41,42]), construction (e.g., predictive maintenance [43,44] and personal protective equipment detection [45,46]), agriculture (e.g., crop and livestock surveillance [47,48], automatic weeding [49,50] and insect detection [51,52]), retail (e.g., self-checkout [53,54] and surveillance for unmanned supermarkets [55,56]) and entertainment (e.g., augmented reality [57,58] and virtual reality [59,60]).…”

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confidence: 99%

Transductive transfer learning for visual recognition

Huang¹

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has given invaluable advice on almost all aspects of my Ph.D. study. I would also express great gratitude towards all the lab-mates in MICL, CSL and SCALE Labs and all the members in Prof Lu's research group for the enthusiastic discussions and priceless support in the last four years. Dayan, Aoran and Pengdeng brought me a lot of precious memory in my Ph.D. journey. Thank Jingyi, Kaiwen, Zichen, Yun and Xueying for their hardworking while we collaborated on projects and researches. I enjoy the good works we have done and the team-working time.Thanks to all of my thesis committee members, Prof. Lin Guosheng, Prof. Liu Ziwei and Prof. Chen Change Loy for the time, kind suggestions and feedback for my qualification examination and thesis. You have all been very patient and kind throughout the whole process, and your encouragement and suggestions have helped refine my work greatly.Last but most importantly, I wish to express the deepest gratitude to my family for their constant and unconditional love and support. Thank you for being always there for me, for listening, for enlightening and for giving me the strength and courage to overcome difficulties and pursue greatness.xi

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“…The dataset used in this use case comprises the measurements across 17-hours period, and was also employed in a previous work developed by Coelho et al (2021). Before generating the histograms to build the GFT, the data was preprocessed and enriched.…”

Section: System and Events Descriptionmentioning

confidence: 99%

“…Stamping presses are subjected to this kind of mechanical failures, which represent one of the main reasons for equipment unavailability or unreliability. This work was developed in the follow-up of the work developed by Coelho et al (2021), where authors developed an innovative approach merging time-series segmentation, anomaly detection and ranking twelve machine/deep learning algorithms. Although the obtained results for some types of failures are quite good, the approach is quite ineffective for other types of failures.…”

Section: Introductionmentioning

confidence: 99%

Reliability Analysis of Sensorized Stamping Presses by Generalized Fault Trees

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Reliability analysis and probability methods are of extreme importance for understanding the behavior of critical systems. In this scope, dynamic fault trees (DFTs) are consolidated graphical models, previously applied at known entities, such as NASA and TESLA. However, the (classical) DFTs analysis has a known issue; the fact that it assumes that the distribution of basic events (BEs) follows the exponential/Weibull distribution, which is often a rough approximation of real data distributions. Moreover, building a DFT model for a real system requires specialized knowledge, to infer the root causes of failures. In this work, a new formalism for the analysis of so-called generic fault trees (GFTs) is proposed which extends DFT to data-driven scenarios, based on the notion of h-approximation of the associated distributions, where leaves may contain an arbitrary compact support distribution or an h-truncation of a distribution. The approach is validated against known solutions in the literature, showing great accuracy. An optimization process is employed to generate the best structure for training a GFT that fits a given data and tested in real use case scenario of a stamping press of Bosch ThermoTechnology. The obtained GFT demonstrates a good fit, considering 3 different metrics.

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Predictive maintenance on sensorized stamping presses by time series segmentation, anomaly detection, and classification algorithms

Cited by 27 publications

References 22 publications

Control System for Automated Technological Process of Hot Stamping—A Case Study

Control System for Automated Technological Process of Hot Stamping—A Case Study

Transductive transfer learning for visual recognition

Reliability Analysis of Sensorized Stamping Presses by Generalized Fault Trees

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