Deep learning and WLC: how to set realistic delivery dates in high variety manufacturing systems

Abstract The manufacturing sector is envisioned to be heavily influenced by artificial-intelligence-based technologies with the extraordinary increases in computational power and data volumes. A central challenge in the manufacturing sector lies in the requirement of a general framework to ensure satisfied diagnosis and monitoring performances in different manufacturing applications. Here, we propose a general data-driven, end-to-end framework for the monitoring of manufacturing systems. This framework, derived from deep-learning techniques, evaluates fused sensory measurements to detect and even predict faults and wearing conditions. This work exploits the predictive power of deep learning to automatically extract hidden degradation features from noisy, time-course data. We have experimented the proposed framework on 10 representative data sets drawn from a wide variety of manufacturing applications. Results reveal that the framework performs well in examined benchmark applications and can be applied in diverse contexts, indicating its potential use as a critical cornerstone in smart manufacturing.

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Deep Learning Approaches to Detect Real Time Events Recognition in Smart Manufacturing Systems – A Short Survey

Advances in Intelligent Networking and Collaborative Systems - Lecture Notes in Networks and Systems ◽

10.1007/978-3-030-84910-8_20 ◽

2021 ◽

pp. 190-194

Author(s):

Suleman Awan ◽

Marcello Trovati

Keyword(s):

Deep Learning ◽

Real Time ◽

Manufacturing Systems ◽

Smart Manufacturing ◽

Learning Approaches ◽

Short Survey ◽

Smart Manufacturing Systems

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Robotic Wireless Sensor Networks, Industrial Artificial Intelligence, and Deep Learning-assisted Smart Process Planning in Sustainable Cyber-Physical Manufacturing Systems

Journal of Self-Governance and Management Economics ◽

10.22381/jsme9420214 ◽

2021 ◽

Vol 9 (4) ◽

pp. 48

Keyword(s):

Artificial Intelligence ◽

Wireless Sensor Networks ◽

Deep Learning ◽

Sensor Networks ◽

Process Planning ◽

Manufacturing Systems ◽

Wireless Sensor

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Deep-Learning-Based Methodology for Fault Diagnosis in Electromechanical Systems

Sensors ◽

10.3390/s20143949 ◽

2020 ◽

Vol 20 (14) ◽

pp. 3949 ◽

Cited By ~ 1

Author(s):

Francisco Arellano-Espitia ◽

Miguel Delgado-Prieto ◽

Victor Martinez-Viol ◽

Juan Jose Saucedo-Dorantes ◽

Roque Alfredo Osornio-Rios

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

Manufacturing Systems ◽

Production Systems ◽

Operating Conditions ◽

Smart Manufacturing ◽

Learning Technology ◽

Electromechanical Systems ◽

Monitoring Strategies ◽

Complex Electromechanical Systems

Fault diagnosis in manufacturing systems represents one of the most critical challenges dealing with condition-based monitoring in the recent era of smart manufacturing. In the current Industry 4.0 framework, maintenance strategies based on traditional data-driven fault diagnosis schemes require enhanced capabilities to be applied over modern production systems. In fact, the integration of multiple mechanical components, the consideration of multiple operating conditions, and the appearance of combined fault patterns due to eventual multi-fault scenarios lead to complex electromechanical systems requiring advanced monitoring strategies. In this regard, data fusion schemes supported with advanced deep learning technology represent a promising approach towards a big data paradigm using cloud-based software services. However, the deep learning models’ structure and hyper-parameters selection represent the main limitation when applied. Thus, in this paper, a novel deep-learning-based methodology for fault diagnosis in electromechanical systems is presented. The main benefits of the proposed methodology are the easiness of application and high adaptability to available data. The methodology is supported by an unsupervised stacked auto-encoders and a supervised discriminant analysis.

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Anomaly Detection and Diagnosis In Manufacturing Systems

Annual Conference of the PHM Society ◽

10.36001/phmconf.2019.v11i1.815 ◽

2019 ◽

Vol 11 (1) ◽

Author(s):

Kalyani Zope ◽

Kuldeep Singh ◽

Sri Harsha Nistala ◽

Arghya Basak ◽

Pradeep Rathore ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Anomaly Detection ◽

Manufacturing Systems ◽

Predictive Maintenance ◽

Sensor Data ◽

Statistical Techniques ◽

Industrial Systems ◽

Learning Techniques ◽

Detection And Diagnosis

Multivariate sensor data collected from manufacturing and process industries represents actual operational behavior and can be used for predictive maintenance of the plants. Anomaly detection and diagnosis, that forms an integral part of predictive maintenance, in industrial systems is however challenging due to their complex behavior, interactions among sensors, corrective actions of control systems and variability in anomalous behavior. While several statistical techniques for anomaly detection have been in use for a long time, these are not particularly suited for temporal (or contextual) anomalies that are characteristic of multivariate time series sensor data. On the other hand, several machine learning and deep learning techniques for anomaly detection gained significant interest in the recent years. Further, anomaly diagnosis that involves localization of the faults did not receive much attention. In this work, we compare the anomaly detection and diagnosis capabilities, in semi-supervised mode, of several statistical, machine learning and deep learning techniques on two systems viz. the interacting quadruple tank system and the continuous stirred tank reactor (CSTR) system both of which are representative of the complexity of large industrial systems. The techniques studied include principal component analysis (PCA), Mahalanobis distance (MD), one-class support vector machine (OCSVM), isolation forest, elliptic envelope, dense auto-encoder and long short term memory auto-encoder (LSTM AE). The study revealed that MD and LSTM-AE have the highest anomaly detection capability, followed closely by PCA and OCSVM. The above techniques also exhibited good diagnosis capability. The study indicates that statistical techniques in spite of their simplicity could be as powerful as machine learning and deep learning techniques, and may be considered for anomaly detection and diagnosis in manufacturing systems.

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Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems

Electronics ◽

10.3390/electronics10202497 ◽

2021 ◽

Vol 10 (20) ◽

pp. 2497

Author(s):

Mihai Andronie ◽

George Lăzăroiu ◽

Mariana Iatagan ◽

Cristian Uță ◽

Roxana Ștefănescu ◽

...

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Internet Of Things ◽

Real Time ◽

Manufacturing Systems ◽

Process Management ◽

Production Systems ◽

Big Data Analytics ◽

Eligibility Criteria ◽

Cyber Physical Production Systems

With growing evidence of deep learning-assisted smart process planning, there is an essential demand for comprehending whether cyber-physical production systems (CPPSs) are adequate in managing complexity and flexibility, configuring the smart factory. In this research, prior findings were cumulated indicating that the interoperability between Internet of Things-based real-time production logistics and cyber-physical process monitoring systems can decide upon the progression of operations advancing a system to the intended state in CPPSs. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout March and August 2021, with search terms including “cyber-physical production systems”, “cyber-physical manufacturing systems”, “smart process manufacturing”, “smart industrial manufacturing processes”, “networked manufacturing systems”, “industrial cyber-physical systems,” “smart industrial production processes”, and “sustainable Internet of Things-based manufacturing systems”. As we analyzed research published between 2017 and 2021, only 489 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 164, chiefly empirical, sources. Subsequent analyses should develop on real-time sensor networks, so as to configure the importance of artificial intelligence-driven big data analytics by use of cyber-physical production networks.

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Pallet Recognition with Multi-Task Learning for Automated Guided Vehicles

Applied Sciences ◽

10.3390/app112411808 ◽

2021 ◽

Vol 11 (24) ◽

pp. 11808

Author(s):

Chunghyup Mok ◽

Insung Baek ◽

Yoonsang Cho ◽

Younghoon Kim ◽

Seoungbum Kim

Keyword(s):

Deep Learning ◽

Control System ◽

Control Systems ◽

Travel Time ◽

Manufacturing Systems ◽

Automated Guided Vehicles ◽

Vision Sensor ◽

Single Task ◽

Laser Sensors ◽

Proposed Model

As the need for efficient warehouse logistics has increased in manufacturing systems, the use of automated guided vehicles (AGVs) has also increased to reduce travel time. The AGVs are controlled by a system using laser sensors or floor-embedded wires to transport pallets and their loads. Because such control systems have only predefined palletizing strategies, AGVs may fail to engage incorrectly positioned pallets. In this study, we consider a vision sensor-based method to address this shortcoming by recognizing a pallet’s position. We propose a multi-task deep learning architecture that simultaneously predicts distances and rotation based on images obtained from a visionary sensor. These predictions complement each other in learning, allowing a multi-task model to learn and execute tasks impossible with single-task models. The proposed model can accurately predict the rotation and displacement of the pallets to derive information necessary for the control system. This information can be used to optimize a palletizing strategy. The superiority of the proposed model was verified by an experiment on images of stored pallets that were collected from a visionary sensor attached to an AGV.

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Characterization of Industry 4.0 Lean Management Problem-Solving Behavioral Patterns Using EEG Sensors and Deep Learning

Sensors ◽

10.3390/s19132841 ◽

2019 ◽

Vol 19 (13) ◽

pp. 2841 ◽

Cited By ~ 7

Author(s):

Javier Villalba-Diez ◽

Xiaochen Zheng ◽

Daniel Schmidt ◽

Martin Molina

Keyword(s):

Deep Learning ◽

Problem Solving ◽

Industry 4.0 ◽

Manufacturing Systems ◽

Behavioral Pattern ◽

Management Problem ◽

Behavioral Patterns ◽

Lean Management ◽

Capture Process

Industry 4.0 leaders solve problems all of the time. Successful problem-solving behavioral pattern choice determines organizational and personal success, therefore a proper understanding of the problem-solving-related neurological dynamics is sure to help increase business performance. The purpose of this paper is two-fold: first, to discover relevant neurological characteristics of problem-solving behavioral patterns, and second, to conduct a characterization of two problem-solving behavioral patterns with the aid of deep-learning architectures. This is done by combining electroencephalographic non-invasive sensors that capture process owners’ brain activity signals and a deep-learning soft sensor that performs an accurate characterization of such signals with an accuracy rate of over 99% in the presented case-study dataset. As a result, the deep-learning characterization of lean management (LM) problem-solving behavioral patterns is expected to help Industry 4.0 leaders in their choice of adequate manufacturing systems and their related problem-solving methods in their future pursuit of strategic organizational goals.

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