Optimization of Edge-PLC-Based Fault Diagnosis With Random Forest in Industrial Internet of Things

Liu, Peng; Zhang, Yifan; Wu, Huifeng; Fu, Tingting

doi:10.1109/jiot.2020.2994200

Cited by 28 publications

(9 citation statements)

References 22 publications

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“…There have been many studies on how to improve the efficiency of edge PLCs. Liu et al [25] proposed a fault diagnosis optimization method for edge PLCs based on a random forest approach to handle feature selection problems, effectively improving the performance of fault diagnosis. Stankovski et al [26] discussed the possibility of applying small PLCs in edge computing and demonstrated it with an example of measuring and monitoring cylinders.…”

Section: Related Workmentioning

confidence: 99%

Memory Allocation Strategy in Edge Programmable Logic Controllers Based on Dynamic Programming and Fixed-Size Allocation

Cheng,

Wan,

Ding

et al. 2023

Applied Sciences

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With the explosive growth of data at the edge in the Industrial Internet of Things (IIoT), edge devices are increasingly performing more data processing tasks to alleviate the load on cloud servers. To achieve this goal, Programmable Logic Controllers (PLCs) are gradually transitioning into edge PLCs. However, efficiently executing a large number of computational tasks in memory-limited edge PLCs is a significant challenge. Therefore, there is a need to design an efficient memory allocation strategy for edge PLCs. This paper proposes a dynamic memory allocation strategy for edge PLCs. It adopts an approach of organizing memory into small blocks to handle memory requests from real-time tasks and utilizes a well-performing dynamic programming method for resource allocation problems to handle memory requests from non-real-time tasks. This approach ensures real-time performance while improving the efficiency of non-real-time task processing. In the simulation experiments, the algorithm implemented based on this allocation strategy is compared with the default method and several open-source memory allocators. The experimental results demonstrate that the proposed algorithm, on average, improves the speed of real-time task processing by 13.7% and achieves a maximum speed improvement of 17.0% for non-real-time task processing. The experimental results show that the allocation strategy effectively improves memory allocation efficiency in memory-limited environments.

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Section: Related Workmentioning

confidence: 99%

Memory Allocation Strategy in Edge Programmable Logic Controllers Based on Dynamic Programming and Fixed-Size Allocation

Cheng,

Wan,

Ding

et al. 2023

Applied Sciences

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“…With the rise of Internet of things (IoT), many of IoT techniques are used in different fields (eg., 5G [13], unmanned aerial vehicle [14], and fault diagnosis [15][16][17][18][19]). Meanwhile, edge computing is a new computing paradigm that enables fast detection through the deployment of algorithms that are embedded in distributed nodes [20].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Quality Inspection of Motor Rotor Using Cost-Effective Intelligent Edge System

Zhu

Wang

et al. 2023

IEEE Internet Things J.

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Induction motors (IMs) are used extensively as driving actuators in electric vehicles. Motor rotors are prone to defects in the die casting procedure, which can significantly reduce the production quality. Benefitting from the development of Internet of things (IoT) techniques and edge computing, this study designed an instrumentation system for the fast inspection of rotor defects to meet the objectives of efficient and high-quality rotor production. First, an electromagnetic sensing device is designed to acquire the induced voltage signal of the rotor under investigation. Second, a residual multiscale feature fusion convolutional neural network model is designed to extract the hierarchical features of the signal, to facilitate defect recognition. The developed algorithm is deployed into a cost-effective edge computing node that includes a signal acquisition circuit and a Raspberry Pi microcontroller. The conducted experimental studies show that this implementation can achieve an inference time of less than 200 ms and accuracy of more than 99 %. It is shown that the designed system exhibits superior performance when compared with conventional methods. The developed, compact and flexible handheld solution with enhanced deep learning techniques shows outstanding potential for use in real-time rotor defect detection.

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“…Considering computing efficiency, authors adopt the edge computing paradigm to process extremely large amounts of data in real time. Also for smart factories, Liu et al [19] introduce an FDD method with random forest based on edge-PLCs. In this paper, edge-PLCs are used to collect sensor data, which reduces communication costs and latency.…”

Section: Introductionmentioning

confidence: 99%

“…Also for smart factories, Liu et al. [19] introduce an FDD method with random forest based on edge‐PLCs. In this paper, edge‐PLCs are used to collect sensor data, which reduces communication costs and latency.…”

Section: Introductionmentioning

confidence: 99%

“…The trained models are deployed and running in edge nodes, which reduces the response latency and energy consumption. There have been some studies that combine edge computing and DL for FDD in different scenarios including smart factory, PV systems, rotating machines and so on [18][19][20][21]. In [18], a DL method for manufacture inspection in smart factories is proposed.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A compressed sensing and CNN‐based method for fault diagnosis of photovoltaic inverters in edge computing scenarios

Wang

Yang

Wang

et al. 2022

IET Renewable Power Gen

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Accurate and real-time diagnosis of the inverter is crucial for the reliability, safety and generation efficiency of the photovoltaic (PV) system. Recently, deep learning (DL) is widely used for accurate diagnosis, which automatically extracts useful features instead of relying on experts. For real-time diagnosis, in an emerging edge computing paradigm, massive data generated by end-device are processed in nearby edge nodes, which reduces energy consumption and latency. However, large amounts of data are required for model training in the cloud, which leads to tremendous burdens of transmission and computation. To tackle these issues, a data-driven diagnosis method based on compressed sensing (CS) and convolutional neural network (CNN) is proposed for open-circuit faults of PV inverters. Based on CS, raw signals are compressed in edge nodes with the optimal compression ratio determined by experiments. The proposed CS-CNN is evaluated on an edge-cloud semi-physical experiment platform. Compared with common compression methods, the accuracy of CS is improved by more than 3%, and the time consumption is only one-fifth. Compared with the CNN of the same structure, the transmission and computation time is largely reduced by an order of magnitude. The developed CS-CNN compresses 85% of data with a test accuracy of 99.18%.

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Optimization of Edge-PLC-Based Fault Diagnosis With Random Forest in Industrial Internet of Things

Cited by 28 publications

References 22 publications

Memory Allocation Strategy in Edge Programmable Logic Controllers Based on Dynamic Programming and Fixed-Size Allocation

Memory Allocation Strategy in Edge Programmable Logic Controllers Based on Dynamic Programming and Fixed-Size Allocation

Real-Time Quality Inspection of Motor Rotor Using Cost-Effective Intelligent Edge System

A compressed sensing and CNN‐based method for fault diagnosis of photovoltaic inverters in edge computing scenarios

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