A contextual sensor system for non-intrusive machine status and energy monitoring

Ren, Yutian; Li, G.P.

doi:10.1016/j.jmsy.2021.11.010

Cited by 9 publications

(3 citation statements)

References 42 publications

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“…A machine-learning pipeline of skeleton-based action recognition is implemented to recognize worker actions with respect to machine states. [27] Concurrently, an unsupervised method is applied on the power signal to detect the state transition of specific machine components, as demonstrated in Reference [28] The design methodology of the two ML agents leverages the causality underlying worker machine interactions to develop two applications. The first application provides a non-intrusive real time monitoring function of the manufacturing workflow with interactive features.…”

Section: Resultsmentioning

confidence: 99%

Smart connected worker edge platform for smart manufacturing: Part 1—Architecture and platform design

Kim

Donovan

Ren

et al. 2022

J Adv Manuf & Process

Self Cite

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The challenge of sustainably producing goods and services for healthy living on a healthy planet requires simultaneous consideration of economic, societal, and environmental dimensions in manufacturing. Enabling technology for data driven manufacturing paradigms like Smart Manufacturing (a.k.a. Industry 4.0) serve as the technological backbone from which sustainable approaches to manufacturing can be implemented. Unfortunately, these technologies are typically associated with broader and deeper factory automation that is often too expensive and complex for the small and medium sized manufacturers (SMMs) that comprise the majority of manufacturing business in the USA and for whom their most valuable asset are the people whose jobs automation while replace. This paper describes an edge intelligent platform to integrate internet-of-things technologies with computing hardware, software, computational workflows for machine learning, and data ingestion, enabling SMMs to transition into smart manufacturing paradigms by leveraging the intelligence of their people. The platform leverages consumer grade electronics and sensors (affordable and portable), customized software with open source software packages (accessible), and existing communication network infrastructures (scalable). The software systems are implemented via Kubernetes orchestration of Docker containerization to ensure scalability and programmability. The platform is adaptive via computational workflow engines that produce information from data by processing with low-cost edge computing devices while efficiently accessing resources of cloud servers as needed. The proposed edge platform connects workers to technological resources that provide computational intelligence (i.e., silicon-based sensing and computation for data collection and contextualization) to enable decision making at the edge of advanced manufacturing.

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Section: Resultsmentioning

confidence: 99%

Smart connected worker edge platform for smart manufacturing: Part 1—Architecture and platform design

Kim

Donovan

Ren

et al. 2022

J Adv Manuf & Process

Self Cite

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“…Model 4 adopts the DANN method [37]. Model 5 adopts an adaptive fault diagnosis network framework [44]. Model 6 adopts the method proposed in this paper.…”

Section: Validation Experiments Amentioning

confidence: 99%

Domain adaptive networks with limited data for rotating machinery fault diagnosis: a case of study of gears

Li,

Yu,

et al. 2023

Meas. Sci. Technol.

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Rotating machinery is one of the most common components in the industry. Therefore, timely and accurate fault diagnosis of rotating machinery is essential for the regular operation of equipment. At present, some achievements have been made in rotating machinery fault diagnosis based on a large number of marked fault data. However, most of the machines are in a normal state in actuality. Especially, the machines run under different loads, so it is costly to collect a large number of labeled fault data under different load distributions. To solve rotating machinery fault diagnosis in different load conditions with limited samples, a domain adaptive group convolutional neural network is proposed. Firstly, the least squares generative adversarial networks were used to expand the limited target sample data. By changing the objective function, the two defects of the low quality of the vibration signal generated by the traditional generative adversarial networks and the unstable training process are optimized. Secondly, the raw vibration signals in the source domain are pre-trained by the group convolutional neural network, and the group training network effectively reduces network parameters. Finally, the source domain signals and target domain signals were trained in domain adversarial networks to diagnose different distributed data in target domains. The proposed method is validated by collecting the raw vibration signals of gears under different loads and different health states, and the effectiveness of the proposed method is proved. Experimental validation shows that the method proposed in this paper achieves an average accuracy improvement of more than 12% compared to other existing methods.

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“…The ML Agent 2 receives real time active power data to detect the machine components' energy states and power consumptions. [16] The ML Agent 2 consists of the event detector module and event classifier module in Figure 2. Firstly, the raw power signal is preprocessed to identify the power events.…”

Section: Agent 1 Andmentioning

confidence: 99%

Smart connected worker edge platform for smart manufacturing: Part 2—Implementation and on‐site deployment case study

Donovan

Kim

Manzo

et al. 2022

J Adv Manuf & Process

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In this paper, we describe specific deployments of the Smart Connected Worker (SCW) Edge Platform for Smart Manufacturing through implementation of four instructive real‐world use cases that illustrate the role of people in a Smart Manufacturing paradigm through which affordable, scalable, accessible, and portable (ASAP) information technology (IT) acquires and contextualizes data into information for transmission to operation technologies (OT). For case one, the platform captures the relationships between energy consumption and human workflows for improved energy productivity while workers interact with machines during semiconductor manufacturing. The platform utilizes human cognition to identify anomalous machine behavior for root cause analysis of system faults via neural network (NN) that recognize alarm postures of workers with cameras. For case two, a smart assembly line is demonstrated for state monitoring and fault detection. Machine learning (ML) models are used to recognize system states and identify fault scenarios with human intervention. For case three, the platform monitors human–machine interactions to classify manufacturing machine states for proper operations and energy productivity. Internal energy states of individual or collections of manufacturing equipment are determined via NN based algorithms that disaggregate signals associated with smart metering typically deployed at manufacturing facilities. These methods predict the real time energy profile of each machine from the total energy profile of a manufacturing site. For case four, a software defined sensor system built with scientific workflow engines is demonstrated for contextualizing data from laser surface refraction for characterization, and diagnostics in the processing of additively manufactured titanium alloy.

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A contextual sensor system for non-intrusive machine status and energy monitoring

Cited by 9 publications

References 42 publications

Smart connected worker edge platform for smart manufacturing: Part 1—Architecture and platform design

Smart connected worker edge platform for smart manufacturing: Part 1—Architecture and platform design

Domain adaptive networks with limited data for rotating machinery fault diagnosis: a case of study of gears

Smart connected worker edge platform for smart manufacturing: Part 2—Implementation and on‐site deployment case study

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