A Recurrent Neural Network Architecture for Failure Prediction in Deep Drawing Sensory Time Series Data

Meyes, Richard; Donauer, Johanna; Schmeing, Andre; Meisen, Tobias

doi:10.1016/j.promfg.2019.06.205

Cited by 25 publications

(10 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wang et al [109] optimized heating systems within buildings, especially offices, in terms of comfort and energy efficiency, using RNN and LSTM. Meyes et al [110] analyzed the product quality during production by predicting component defects with an RNN.…”

Section: Resultsmentioning

confidence: 99%

“…-Predictive Maintenance [57,59,69,70,80,83,84,86,95,113]; -Production planning [52,54,61,65,72,77,78,101,102]; -Fault detection and prediction/predictive quality [58,62,74,82,87,89,93,94,110,111,115,118]; -Increasing energy efficiency in production [56,63,85,99,100,103,108,114,119] and facility management [53,67,76,107,109].…”

Section: Identification Of Typical Use Cases Of Ai Application Increasing Resource Efficiencymentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

et al. 2021

View full text Add to dashboard Cite

Sustainability improvements in industrial production are essential for tackling climate change and the resulting ecological crisis. In this context, resource efficiency can directly lead to significant advancements in the ecological performance of manufacturing companies. The application of Artificial Intelligence (AI) also plays an increasingly important role. However, the potential influence of AI applications on resource efficiency has not been investigated. Against this background, this article provides an overview of thecurrent AI applications and how they affect resource efficiency. In line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this paper identifies, categorizes, and analyzes seventy papers with a focus on AI tasks, AI methods, business units, and their influence on resource efficiency. Only a minority of papers was found to address resource efficiency as an explicit objective. Subsequently, typical use cases of the identified AI applications are described with a focus on predictive maintenance, production planning, fault detection and predictive quality, as well as the increase in energy efficiency. In general, more research is needed that explicitly considers sustainability in the development and use phase of AI solutions, including Green AI. This paper contributes to research in this field by systematically examining papers and revealing research deficits. Additionally, practitioners are offered the first indications of AI applications increasing resource efficiency.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Identification Of Typical Use Cases Of Ai Application Increasing Resource Efficiencymentioning

confidence: 99%

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Some application subjects of predictive quality systems are proposed in the literature. Such as, the deep drawing manufacturing process of car body parts [8], tool flank wears at a turning operation [9], refrigerant brazed plate heat exchangers (BPHE) [10], battery cells production [11]. Moreover, we can add topics to consider like crankshaft production line [12], rare quality event detection, ultrasonic metal welding of battery tabs, sensorless drive diagnosis [14].…”

Section: Related Workmentioning

confidence: 99%

“…Digitalization needs specific subjects for using machine learning algorithms aspect of predictive quality applications. Also, these special concepts need a wide range and different type of variables such as; cutting speed (rpm), feed rate (mm/rev), depth of cut (mm), lubrication variables [9], plate geometry, operating conditions [10], x-ray inspection with height, % shape 2D, % shape 3D, % surface, % volume, % offset X µm, offset Y µm [13], flange retraction laser data, strain gauge sensory data, signal data, the occurrence of process failures [8], etc.…”

Section: Related Workmentioning

confidence: 99%

Predictive Quality Defect Detection Using Machine Learning Algorithms: A Case Study from Automobile Industry

Yorulmuş

Bolat

Bahadır

2021

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Industry 4.0 is generally defined as a development system that compels the digitalization of processes to create integrated and autonomous systems. The process tracking of parts is very important in terms of detecting missed faulty products. Some defects that escape from quality control directly affect the enduser. Machine learning algorithms have been used to predict changes in the quality control processes and defective products, toward real-time and effective data processing. Thus, the highest quality of the final product will be delivered to the customer and to reduce the defective production coming out of the manufacturing chain. In this article, the study aims to establish a predictive quality model that can detect defect-free approved but faulty products overlooked during the quality inspection operations. Machine learning methods are used to analyze the relationship between quality control data and customer complaints. For this purpose, we use the last quality stage data of an automobile manufacturer's brake system from 2018 to 2020. Machine learning models are constructed using logistic regression, ridge regression, support vector machine, random forest classification tree, gradient boost, XGBoost, LightGBM, and CatBoost algorithms. The results of specificity and negative prediction value show that the Gradient Boost and CatBoost algorithms have the best classification benefit for detecting the rare events.

show abstract

“…As an example to show the effectiveness of the proposed architecture, we report the creation of a Long Short-Term Memory (LSTM) model for failure detection of a specific railway point along the railway line Milano-Monza-Chiasso. LSTM models are a special kind of Recurrent Neural Networks (RNN) widely employed by both Academia and Industries for failure prediction [6]. A key aspect of RNN is their ability to store information or cell state for use later to make new predictions.…”

Section: Example Of Failure Detection Using Lstmmentioning

confidence: 99%

An Architecture for Predictive Maintenance of Railway Points Based on Big Data Analytics

Salierno

Morvillo²,

Leonardi

et al. 2020

Lecture Notes in Business Information Processing

View full text Add to dashboard Cite

Massive amounts of data produced by railway systems are a valuable resource to enable Big Data analytics. Despite its richness, several challenges arise when dealing with the deployment of a big data architecture into a railway system. In this paper, we propose a four-layers big data architecture with the goal of establishing a data management policy to manage massive amounts of data produced by railway switch points and perform analytical tasks efficiently. An implementation of the architecture is given along with the realization of a Long Short-Term Memory prediction model for detecting failures on the Italian Railway Line of Milano -Monza -Chiasso.

show abstract

A Recurrent Neural Network Architecture for Failure Prediction in Deep Drawing Sensory Time Series Data

Cited by 25 publications

References 13 publications

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

Predictive Quality Defect Detection Using Machine Learning Algorithms: A Case Study from Automobile Industry

An Architecture for Predictive Maintenance of Railway Points Based on Big Data Analytics

Contact Info

Product

Resources

About