Functional Safety and Industrial Communication Associated for the Smart Factory

Tran, Thanh Kha; Yahoui, Hamed; Siauve, Nicolas

doi:10.1109/eaeeie.2017.8768714

Cited by 4 publications

(2 citation statements)

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“…incidents. There exists a relation between safety leadership, culture, climate, behavior, and the overall performance of an organization [157]- [160]. The provision of prior information based on events that have already occurred influences the safety and performance of organizations.…”

Section: B Safety Management Using Iiot -An Overviewmentioning

confidence: 99%

Industrial Internet of Things for Safety Management Applications: A Survey

et al. 2022

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Industrial Internet of Things (IIoT) aims to achieve higher operational and management efficiencies by bridging machinery, equipment, human resources, and all other actors involved in an industrial environment. This bridging enables data flow over an often complex and heterogeneous communication network. It enables timely decision-making, which affects various aspects of the organization such as business, operations, maintenance, safety, stock, and logistics. Despite the plethora of works in the domain of IIoT dealing with the above aspects, very few works deal with safety in industries. Industrial safety, especially whenever it is intertwined with the safety of humans, is a critical domain and holds much scope for improvement in the context of IIoT-based solutions for industrial safety management. Through this survey, we provide a comprehensive overview of the safety issues prevalent in the industries. Subsequently, we classify and provide an in-depth analysis of the safety aspects in various application areas of IIoT such as healthcare, transportation, manufacturing, and mining. Finally, we examine the research gaps in various domains and recommend future research directions. We discuss diverse forms of technologies, prototypes, systems, models, methods, and applications to ensure the safety of individuals and the risks associated with them. The primary aim of this work is to analyze and synthesize the existing researches and acknowledge the applicability of these research works towards safety management using IIoT.

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Section: B Safety Management Using Iiot -An Overviewmentioning

confidence: 99%

Industrial Internet of Things for Safety Management Applications: A Survey

et al. 2022

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“…On the other hand, safety can be described as the "condition of the system operating without causing unacceptable risk of physical injury or damage to the health of the people, either directly or indirectly, as a result of damage to property or to the environment" [20]. Two broad approaches in ensuring safety in the industries include: "intrinsic" and "active" measure [21]. In the intrinsic approach, workers and machines are separated by physically putting them in various locations, locked door, or through the use of specialized insulation.…”

Section: Safety and Security: Establishing The Differencementioning

confidence: 99%

Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor

et al. 2021

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Factory shop floor workers are exposed to threats and accidents due to their encounters with tools, equipment, and toxic materials. There are cases of occupational accidents resulting in injuries to workers and precipitating lawsuits, which on the other hand affect company’s operational cost. To ensure the safety of workers within the shop floor, there is a need for proactive activity monitoring. Such activities include detection of falling objects, abnormal vibration, and movement of humans within an acceptable area of the factory floor. Breathing sensor-based monitoring of workers in the smart factory shop floor can also be implemented. This is for the detection of human activity, especially in cases where workers are in isolation with no available emergency assistance. Internet of Things (IoT), Industrial Internet of Things (IIoT), and machine learning (ML) have enabled so many possibilities in this area. In this study, we present a simple test-bed, which is made up of a vibration sensor, a breathing and movement sensor, and a Light Detection and Ranging (LIDAR) sensor. These sensors were used to gather normal and abnormal data of human activities at the factory. We developed a dataset based on possible real-life situations and it is made up of about 10,000 data points. The data was split with a ratio of 75:25 for training and testing the model. We investigated the performance of different ML algorithms, including support vector machine (SVM), linear regression, naive Bayes (NB), K-nearest neighbor (KNN), and convolutional neural network (CNN). From our experiments, the CNN model outperformed other algorithms with an accuracy of 99.45%, 99.78%,100%, and 100%, respectively, for vibration, movement, breathing, and distance. We have also successfully developed a dataset to assist the research community in this field.

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