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JIDOKA. Integration of Human and AI within Industry 4.0 Cyber Physical Manufacturing Systems

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 50704

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Prof. Dr. Javier Villalba-Diez

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1. Hochschule Heilbronn, Campus Schwäbisch Hall, 74081 Heilbronn, Germany
2. Artificial Intelligence Department, Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, 28660 Boadilla del Monte, Spain
Interests: artificial intelligence; deep learning; cyberphysical systems; business intelligence; strategic organizational design
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Prof. Dr. Joaquin Ordieres Meré

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Guest Editor

Escuela Técnica Superior de Ingenieros Industriales (ETSII), Universidad Politécnica de Madrid, José Gutiérrez Abascal 2, 28006 Madrid, Spain
Interests: big data analytics; IIoT; smart sensors; digital transformation of industry; artificial intelligence; machine learning; distributed computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today’s manufacturing industry is increasingly subject to a global competition due to falling transportation and communication costs as well as a faster transportation of goods. Adding to this, it is becoming more and more important for companies to reduce their environmental footprint by cutting down energy and material usage in order to reach a sustainable level of the consumed resources. This all increases the pressure to find ways in which to reduce the costs but, at the same time, increase the speed of the delivered goods.

While it is quite generally accepted that higher automation naturally improves the quality in manufacturing, the effects are not necessarily positive and, at least on their own, should not be seen as a sufficient step towards higher quality in manufacturing. In some cases when the complex interplay between machine and human operator is not fully taken into consideration, this can even expand the problems due to the complexity of the task. In fact, automatization ought to respond to the call of integrating the advantageous capabilities of both human and cyberphysical assets. This is where JIDOKA comes into play: “automation with a human touch”.

This allows for two general strategies to deal with the complexity that do not—and should not—need to be used exclusively. The first is to reduce the complexity of a process step by analyzing it and using tools such as lean management. A different approach is to use tools with a higher complexity than the problem in order to try to control it. At this point, artificial intelligence (AI) comes into play. This includes a big range of subfields with different goals: from the integration of smart wearable sensors to ease human decision making in the value creation process to distributed ledger technologies that increase the trustworthiness of the systems in place; from the ubiquitous acquisition of relevant data with industrial internet of things to the online computation of such data to sharpen strategic market positioning and responsiveness to customer needs; and from systematic human problem-solving empowerment to discriminating deep learning algorithms.

The contributions presented in this Special Issue should combine the application of lean management systematics with artificial intelligence methodologies within the context of cyberphysical systems.

Some of the areas of interest (amongst others) include:

JIDOKA—intelligent automation with a human touch;
Predictive maintenance—online monitoring, condition-based maintenance, risk-based maintenance;
IIoT related to production, safety, and/or health in the workplace, including pollution;
Industrial applications of smart sensors in, e.g., cloud computing, mobile technologies, 3D printing, advanced robotics, big data, internet of things, RFID technology, and cognitive computing, that enable better value stream performance;
Applications of smart sensors that optimize the energy consumption of value creation processes and reduce the CO₂ manufacturing footprint, i.e., smart grids;
IIoT and integration between workers and process automation to produce a more comprehensive perspective;
Industrial applications of cloud computing, artificial intelligence, machine learning, and deep learning that enable a better value stream performance within smart sensor networks;
Applications of smart sensor networks to cyberphysical production systems;
Applications of deep learning to industrial problem solving and value stream continuous improvement;
Complex networked lean production systems;
Trust and accountability through DLT (distributed ledger technology) in industrial applications.

Dr. Javier Villalba Diez
Dr. Joaquin Ordieres Meré
Guest Editors

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Keywords

JIDOKA
lean management
cyberphysical production systems
deep learning applied to smart sensor networks
predictive maintenance
smart sensors in industrial applications
industrial internet of things
cloud computing
3D printing
advanced robotics
big data in industrial applications
RFID technology
cognitive computing
deep learning
smart grid
artificial intelligence in industrial applications
distributed ledger technology in industrial applications

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Published Papers (10 papers)

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11 pages, 1036 KiB

Open AccessCommunication

A Framework for the Optimization of Complex Cyber-Physical Systems via Directed Acyclic Graph

by Manuel Castejón-Limas, Laura Fernández-Robles, Héctor Alaiz-Moretón, Jaime Cifuentes-Rodriguez and Camino Fernández-Llamas

Sensors 2022, 22(4), 1490; https://doi.org/10.3390/s22041490 - 15 Feb 2022

Cited by 1 | Viewed by 2366

Abstract

Mathematical modeling and data-driven methodologies are frequently required to optimize industrial processes in the context of Cyber-Physical Systems (CPS). This paper introduces the PipeGraph software library, an open-source python toolbox for easing the creation of machine learning models by using Directed Acyclic Graph (DAG)-like implementations that can be used for CPS. scikit-learn’s Pipeline is a very useful tool to bind a sequence of transformers and a final estimator in a single unit capable of working itself as an estimator. It sequentially assembles several steps that can be cross-validated together while setting different parameters. Steps encapsulation secures the experiment from data leakage during the training phase. The scientific goal of PipeGraph is to extend the concept of Pipeline by using a graph structure that can handle scikit-learn’s objects in DAG layouts. It allows performing diverse operations, instead of only transformations, following the topological ordering of the steps in the graph; it provides access to all the data generated along the intermediate steps; and it is compatible with GridSearchCV function to tune the hyperparameters of the steps. It is also not limited to

(X, y)

entries. Moreover, it has been proposed as part of the scikit-learn-contrib supported project, and is fully compatible with scikit-learn. Documentation and unitary tests are publicly available together with the source code. Two case studies are analyzed in which PipeGraph proves to be essential in improving CPS modeling and optimization: the first is about the optimization of a heat exchange management system, and the second deals with the detection of anomalies in manufacturing processes. Full article

(This article belongs to the Special Issue JIDOKA. Integration of Human and AI within Industry 4.0 Cyber Physical Manufacturing Systems)

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17 pages, 868 KiB

Open AccessArticle

Optimisation of Maintenance Policies Based on Right-Censored Failure Data Using a Semi-Markovian Approach

by Antonio Sánchez-Herguedas, Angel Mena-Nieto, Francisco Rodrigo-Muñoz, Javier Villalba-Díez and Joaquín Ordieres-Meré

Sensors 2022, 22(4), 1432; https://doi.org/10.3390/s22041432 - 13 Feb 2022

Cited by 7 | Viewed by 2287

Abstract

This paper exposes the existing problems for optimal industrial preventive maintenance intervals when decisions are made with right-censored data obtained from a network of sensors or other sources. A methodology based on the use of the z transform and a semi-Markovian approach is presented to solve these problems and obtain a much more consistent mathematical solution. This methodology is applied to a real case study of the maintenance of large marine engines of vessels dedicated to coastal surveillance in Spain to illustrate its usefulness. It is shown that the use of right-censored failure data significantly decreases the value of the optimal preventive interval calculated by the model. In addition, that optimal preventive interval increases as we consider older failure data. In sum, applying the proposed methodology, the maintenance manager can modify the preventive maintenance interval, obtaining a noticeable economic improvement. The results obtained are relevant, regardless of the number of data considered, provided that data are available with a duration of at least 75% of the value of the preventive interval. Full article

(This article belongs to the Special Issue JIDOKA. Integration of Human and AI within Industry 4.0 Cyber Physical Manufacturing Systems)

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10 pages, 2754 KiB

Open AccessCommunication

Improvement of Quantum Approximate Optimization Algorithm for Max–Cut Problems

by Javier Villalba-Diez, Ana González-Marcos and Joaquín B. Ordieres-Meré

Sensors 2022, 22(1), 244; https://doi.org/10.3390/s22010244 - 30 Dec 2021

Cited by 7 | Viewed by 3145

Abstract

The objective of this short letter is to study the optimal partitioning of value stream networks into two classes so that the number of connections between them is maximized. Such kind of problems are frequently found in the design of different systems such as communication network configuration, and industrial applications in which certain topological characteristics enhance value–stream network resilience. The main interest is to improve the Max–Cut algorithm proposed in the quantum approximate optimization approach (QAOA), looking to promote a more efficient implementation than those already published. A discussion regarding linked problems as well as further research questions are also reviewed. Full article

(This article belongs to the Special Issue JIDOKA. Integration of Human and AI within Industry 4.0 Cyber Physical Manufacturing Systems)

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17 pages, 12396 KiB

Open AccessArticle

Human–Machine Integration in Processes within Industry 4.0 Management

by Javier Villalba-Diez and Joaquín Ordieres-Meré

Sensors 2021, 21(17), 5928; https://doi.org/10.3390/s21175928 - 3 Sep 2021

Cited by 9 | Viewed by 4593

Abstract

The aim of this work is to use IIoT technology and advanced data processing to promote integration strategies between these elements to achieve a better understanding of the processing of information and thus increase the integrability of the human–machine binomial, enabling appropriate management strategies. Therefore, the major objective of this paper is to evaluate how human–machine integration helps to explain the variability associated with value creation processes. It will be carried out through an action research methodology in two different case studies covering different sectors and having different complexity levels. By covering cases from different sectors and involving different value stream architectures, with different levels of human influence and organisational requirements, it will be possible to assess the transparency increases reached as well as the benefits of analysing processes with higher level of integration between them. Full article

(This article belongs to the Special Issue JIDOKA. Integration of Human and AI within Industry 4.0 Cyber Physical Manufacturing Systems)

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14 pages, 4216 KiB

Open AccessCommunication

Quantum JIDOKA. Integration of Quantum Simulation on a CNC Machine for In–Process Control Visualization

by Javier Villalba-Diez, Miguel Gutierrez, Mercedes Grijalvo Martín, Tomas Sterkenburgh, Juan Carlos Losada and Rosa María Benito

Sensors 2021, 21(15), 5031; https://doi.org/10.3390/s21155031 - 24 Jul 2021

Cited by 7 | Viewed by 4091

Abstract

With the advent of the Industry 4.0 paradigm, the possibilities of controlling manufacturing processes through the information provided by a network of sensors connected to work centers have expanded. Real-time monitoring of each parameter makes it possible to determine whether the values yielded by the corresponding sensor are in their normal operating range. In the interplay of the multitude of parameters, deterministic analysis quickly becomes intractable and one enters the realm of “uncertain knowledge”. Bayesian decision networks are a recognized tool to control the effects of conditional probabilities in such systems. However, determining whether a manufacturing process is out of range requires significant computation time for a decision network, thus delaying the triggering of a malfunction alarm. From its origins, JIDOKA was conceived as a means to provide mechanisms to facilitate real-time identification of malfunctions in any step of the process, so that the production line could be stopped, the cause of the disruption identified for resolution, and ultimately the number of defective parts minimized. Our hypothesis is that we can model the internal sensor network of a computer numerical control (CNC) machine with quantum simulations that show better performance than classical models based on decision networks. We show a successful test of our hypothesis by implementing a quantum digital twin that allows for the integration of quantum computing and Industry 4.0. This quantum digital twin simulates the intricate sensor network within a machine and permits, due to its high computational performance, to apply JIDOKA in real time within manufacturing processes. Full article

(This article belongs to the Special Issue JIDOKA. Integration of Human and AI within Industry 4.0 Cyber Physical Manufacturing Systems)

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22 pages, 5161 KiB

Open AccessArticle

Quantum Strategic Organizational Design: Alignment in Industry 4.0 Complex-Networked Cyber-Physical Lean Management Systems

by Javier Villalba-Diez and Xiaochen Zheng

Sensors 2020, 20(20), 5856; https://doi.org/10.3390/s20205856 - 16 Oct 2020

Cited by 23 | Viewed by 4047

Abstract

The strategic design of organizations in an environment where complexity is constantly increasing, as in the cyber-physical systems typical of Industry 4.0, is a process full of uncertainties. Leaders are forced to make decisions that affect other organizational units without being sure that their decisions are the right ones. Previously to this work, genetic algorithms were able to calculate the state of alignment of industrial processes that were measured through certain key performance indicators (KPIs) to ensure that the leaders of the Industry 4.0 make decisions that are aligned with the strategic objectives of the organization. However, the computational cost of these algorithms increases exponentially with the number of KPIs. That is why this work makes use of the principles of quantum computing to present the strategic design of organizations from a novel point of view: Quantum Strategic Organizational Design (QSOD). The effectiveness of the application of these principles is shown with a real case study, in which the computing time is reduced from hundreds of hours to seconds. This has very powerful practical applications for industry leaders, since, with this new approach, they can potentially allow a better understanding of the complex processes underlying the strategic design of organizations and, above all, make decisions in real-time. Full article

(This article belongs to the Special Issue JIDOKA. Integration of Human and AI within Industry 4.0 Cyber Physical Manufacturing Systems)

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22 pages, 2965 KiB

Open AccessArticle

Data Handling in Industry 4.0: Interoperability Based on Distributed Ledger Technology

by Shengjing Sun, Xiaochen Zheng, Javier Villalba-Díez and Joaquín Ordieres-Meré

Sensors 2020, 20(11), 3046; https://doi.org/10.3390/s20113046 - 27 May 2020

Cited by 60 | Viewed by 10184

Abstract

Information-intensive transformation is vital to realize the Industry 4.0 paradigm, where processes, systems, and people are in a connected environment. Current factories must combine different sources of knowledge with different technological layers. Taking into account data interconnection and information transparency, it is necessary to enhance the existing frameworks. This paper proposes an extension to an existing framework, which enables access to knowledge about the different data sources available, including data from operators. To develop the interoperability principle, a specific proposal to provide a (public and encrypted) data management solution to ensure information transparency is presented, which enables semantic data treatment and provides an appropriate context to allow data fusion. This proposal is designed also considering the Privacy by Design option. As a proof of application case, an implementation was carried out regarding the logistics of the delivery of industrial components in the construction sector, where different stakeholders may benefit from shared knowledge under the proposed architecture. Full article

(This article belongs to the Special Issue JIDOKA. Integration of Human and AI within Industry 4.0 Cyber Physical Manufacturing Systems)

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25 pages, 39702 KiB

Open AccessArticle

Industry 4.0 Lean Shopfloor Management Characterization Using EEG Sensors and Deep Learning

by Daniel Schmidt, Javier Villalba Diez, Joaquín Ordieres-Meré, Roman Gevers, Joerg Schwiep and Martin Molina

Sensors 2020, 20(10), 2860; https://doi.org/10.3390/s20102860 - 18 May 2020

Cited by 32 | Viewed by 6401

Abstract

Achieving the shift towards Industry 4.0 is only feasible through the active integration of the shopfloor into the transformation process. Several shopfloor management (SM) systems can aid this conversion. They form two major factions. The first includes methodologies such as Balanced Scorecard (BSC). A defining feature is rigid structures to fixate on pre-defined goals. Other SM strategies instead concentrate on continuous improvement by giving directions. An example of this group is the “HOSHIN KANRI TREE” (HKT). One way of analyzing the dissimilarities, the advantages and disadvantages of these groups, is to examine the neurological patterns of workers as they are applying these. This paper aims to achieve this evaluation through non-invasive electroencephalography (EEG) sensors, which capture the electrical activity of the brain. A deep learning (DL) soft sensor is used to classify the recorded data with an accuracy of 96.5%. Through this result and an analysis using the correlations of the EEG signals, it has been possible to detect relevant characteristics and differences in the brain’s activity. In conclusion, these findings are expected to help assess SM systems and give guidance to Industry 4.0 leaders. Full article

(This article belongs to the Special Issue JIDOKA. Integration of Human and AI within Industry 4.0 Cyber Physical Manufacturing Systems)

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21 pages, 11304 KiB

Open AccessArticle

Healthy Operator 4.0: A Human Cyber–Physical System Architecture for Smart Workplaces

by Shengjing Sun, Xiaochen Zheng, Bing Gong, Jorge García Paredes and Joaquín Ordieres-Meré

Sensors 2020, 20(7), 2011; https://doi.org/10.3390/s20072011 - 3 Apr 2020

Cited by 70 | Viewed by 8352

Abstract

Recent advances in technology have empowered the widespread application of cyber–physical systems in manufacturing and fostered the Industry 4.0 paradigm. In the factories of the future, it is possible that all items, including operators, will be equipped with integrated communication and data processing capabilities. Operators can become part of the smart manufacturing systems, and this fosters a paradigm shift from independent automated and human activities to human–cyber–physical systems (HCPSs). In this context, a Healthy Operator 4.0 (HO4.0) concept was proposed, based on a systemic view of the Industrial Internet of Things (IIoT) and wearable technology. For the implementation of this relatively new concept, we constructed a unified architecture to support the integration of different enabling technologies. We designed an implementation model to facilitate the practical application of this concept in industry. The main enabling technologies of the model are introduced afterward. In addition, a prototype system was developed, and relevant experiments were conducted to demonstrate the feasibility of the proposed system architecture and the implementation framework, as well as some of the derived benefits. Full article

(This article belongs to the Special Issue JIDOKA. Integration of Human and AI within Industry 4.0 Cyber Physical Manufacturing Systems)

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10 pages, 2309 KiB

Open AccessLetter

Industry 4.0 Quantum Strategic Organizational Design Configurations. The Case of Two Qubits: One Reports to One

by Javier Villalba-Diez, Rosa María Benito and Juan Carlos Losada

Sensors 2020, 20(23), 6977; https://doi.org/10.3390/s20236977 - 6 Dec 2020

Cited by 6 | Viewed by 2673

Abstract

In this paper we investigate how the relationship with a subordinate who reports to him influences the alignment of an Industry 4.0 leader. We do this through the implementation of quantum circuits that represent decision networks. In fact, through the quantum simulation of strategic organizational design configurations (QSOD) through five hundred simulations of quantum circuits, we conclude that there is an influence of the subordinate on the leader that resembles that of a harmonic under-damped oscillator around the value of 50% probability of alignment for the leader. Likewise, we have observed a fractal behavior in this type of relationship, which seems to conjecture that there is an exchange of energy between the two agents that oscillates with greater or lesser amplitude depending on certain parameters of interdependence. Fractality in this QSOD context allows for a quantification of these complex dynamics and its pervasive effect offers robustness and resilience to the two-qubit interaction. Full article

(This article belongs to the Special Issue JIDOKA. Integration of Human and AI within Industry 4.0 Cyber Physical Manufacturing Systems)

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