Industrial AI and predictive analytics for smart manufacturing systems

Lee, Jay; Singh, Jaskaran; Azamfar, Moslem; Pandhare, Vibhor

doi:10.1016/b978-0-12-820027-8.00008-3

Cited by 21 publications

(21 citation statements)

References 46 publications

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“…Thus, most of the time, it means working with time-series, which are difficult and more complicated to manage because they need to be processed extensively. More issues related to the characteristics of industrial data were identified in [17,18]:…”

Section: Adaptation Of Ai To Industry (Consumer Ai Vs Industrial Ai)mentioning

confidence: 99%

Data-Driven Fault Diagnosis for Electric Drives: A Review

Gonzalez-Jimenez

Olmo

Poza

et al. 2021

Sensors

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The need to manufacture more competitive equipment, together with the emergence of the digital technologies from the so-called Industry 4.0, have changed many paradigms of the industrial sector. Presently, the trend has shifted to massively acquire operational data, which can be processed to extract really valuable information with the help of Machine Learning or Deep Learning techniques. As a result, classical Condition Monitoring methodologies, such as model- and signal-based ones are being overcome by data-driven approaches. Therefore, the current paper provides a review of these data-driven active supervision strategies implemented in electric drives for fault detection and diagnosis (FDD). Hence, first, an overview of the main FDD methods is presented. Then, some basic guidelines to implement the Machine Learning workflow on which most data-driven strategies are based, are explained. In addition, finally, the review of scientific articles related to the topic is provided, together with a discussion which tries to identify the main research gaps and opportunities.

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Section: Adaptation Of Ai To Industry (Consumer Ai Vs Industrial Ai)mentioning

confidence: 99%

Data-Driven Fault Diagnosis for Electric Drives: A Review

Gonzalez-Jimenez

Olmo

Poza

et al. 2021

Sensors

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“…Modern machines are often equipped with remote sensors and diagnostics to alert operators to problems as they occur. Predictive AI technology can highlight problems before they occur and enact steps to mitigate them, which in turn reduces their impact on cost and efficiency [36].…”

Section: Smart Manufacturingmentioning

confidence: 99%

“…Commonly referred to as 3D printing, AM is a "technology that applies the additiveshaping principle and therefore builds physical 3D geometries by successive addition of material" [36]. Since its invention in 1986 by Charles Hull (founder of the stereolithography process), it has gained significant academic and industrial interest due to its ability to create complex and customizable structures [37].…”

Section: Additive Manufacturingmentioning

confidence: 99%

Smart Additive Manufacturing: The Path to the Digital Value Chain

2021

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The aim of this article is to characterize the impacts of Smart Additive Manufacturing (SAM) on industrial production, digital supply chains (DSCs) and corresponding digital value chains (DVCs), logistics and inventory management. The method used consists of a critical review of the literature, enriched by the authors’ field experience. The results show that digital transformation of manufacturing is affecting business models, from resource acquisition to the end user. Smart manufacturing is considered a successful improvement introduced by Industry 4.0. Additive Manufacturing (AM) plays a crucial role in this digital transformation, changing the way manufacturers think about the entire lifecycle of a product. SAM combines AM in a smart factory environment. SAM reduces the complexity of DSCs and contributes to a more flexible approach to logistics and inventory management. It has also spurred the growth and popularization of customized mass production as well as decentralized manufacturing, rapid prototyping, unprecedented flexibility in product design, production and delivery, and resource efficiency and sustainability. SAM technology impacts all five Fletcher’s stages in DVCs. However, the need for clear definitions and regulations on 3D printing of digital files and their reproduction, as well as product health, safety, and integrity issues, cannot be ignored. Furthermore, investment in this technology is still expensive and can be prohibitive for many companies, namely SMEs.

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“…This intelligence allows them to analyze data and make decisions similar to how a human brain does the same. Since the essence of IoT devices is to gather data and make use of it, placing data obtained from physical devices through machine learning and artificial intelligence allows us to expand upon those processes [7]. The Internet of Intelligent Things (IoIT) uses artificial intelligence to bring more value to the IoT domain by better interpreting data obtained from connected devices.…”

Section: Background Informationmentioning

confidence: 99%

The Emergence of Artificial Intelligence for Industrial Internet of Thing Engagement

2021

IJATCSE

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New IT technologies will help enterprises across all industries to master future challenges. As AI emerges from science fiction to become the frontier of world changing technologies, there is an urgent need for systematic development and implementation of AI to see its real impact in the next generation of industrial systems, namely Industry 4.0. Today, the term “industrial automation” is generally referred to in the context of Industry 4.0 and the Industrial Internet of Things the two most recent technological revolutions in the industrial sector. The core principles of Industry 4.0 focus on increasing productivity, cost efficiency, quality, and safety by utilizing innovative technologies enabled by the IIoT, such as cyberphysical systems, cloud computing, big data, artificial intelligence and machine learning. When implementing this technology by using data centric digital business model. Internet of Things & Artificial Intelligence are poised to transform industrial operations.

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Industrial AI and predictive analytics for smart manufacturing systems

Cited by 21 publications

References 46 publications

Data-Driven Fault Diagnosis for Electric Drives: A Review

Data-Driven Fault Diagnosis for Electric Drives: A Review

Smart Additive Manufacturing: The Path to the Digital Value Chain

The Emergence of Artificial Intelligence for Industrial Internet of Thing Engagement

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