Anomaly Detection Approach in Industrial Control Systems Based on Measurement Data

Zhao, Xianfeng; Zhang, Lei; Cao, Yixin; Jin, Kai; Hou, Yupeng

doi:10.3390/info13100450

Cited by 7 publications

(4 citation statements)

References 27 publications

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“…ML techniques have become increasingly prominent in AD due to their capability to manage complex data patterns [22,23]:…”

Section: Machine Learning-based Methodsmentioning

confidence: 99%

An Anomaly Detection Approach to Determine Optimal Cutting Time in Cheese Formation

Loddo,

Ghiani,

Perniciano

et al. 2024

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The production of cheese, a beloved culinary delight worldwide, faces challenges in maintaining consistent product quality and operational efficiency. One crucial stage in this process is determining the precise cutting time during curd formation, which significantly impacts the quality of the cheese. Misjudging this timing can lead to the production of inferior products, harming a company’s reputation and revenue. Conventional methods often fall short of accurately assessing variations in coagulation conditions due to the inherent potential for human error. To address this issue, we propose an anomaly-detection-based approach. In this approach, we treat the class representing curd formation as the anomaly to be identified. Our proposed solution involves utilizing a one-class, fully convolutional data description network, which we compared against several state-of-the-art methods to detect deviations from the standard coagulation patterns. Encouragingly, our results show F1 scores of up to 0.92, indicating the effectiveness of our approach.

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“…ML techniques have become increasingly prominent in AD due to their capability to manage complex data patterns [22,23]:…”

Section: Machine Learning-based Methodsmentioning

confidence: 99%

An Anomaly Detection Approach to Determine Optimal Cutting Time in Cheese Formation

Loddo,

Ghiani,

Perniciano

et al. 2024

Information

View full text Add to dashboard Cite

show abstract

“…In general, the definition of learning accuracy depends on the task for which the ML model has been employed. For example, for anomalous behavior detection [339] and object detection [25] tasks, MLA may be the percentage of correct predictions an ML model makes. Meanwhile, for tasks such as PV power prediction [291], learning accuracy can either be defined by the RMSE or MAE among the predictions and actual observations.…”

Section: ) Accuracymentioning

confidence: 99%

Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions

López,

Rosabal,

Ruiz-Guirola

et al. 2023

IEEE Open J. Commun. Soc.

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Technology solutions must effectively balance economic growth, social equity, and environmental integrity to achieve a sustainable society. Notably, although the Internet of Things (IoT) paradigm constitutes a key sustainability enabler, critical issues such as the increasing maintenance operations, energy consumption, and manufacturing/disposal of IoT devices have long-term negative economic, societal, and environmental impacts and must be efficiently addressed. This calls for self-sustainable IoT ecosystems requiring minimal external resources and intervention, effectively utilizing renewable energy sources, and recycling materials whenever possible, thus encompassing energy sustainability. In this work, we focus on energy-sustainable IoT during the operation phase, although our discussions sometimes extend to other sustainability aspects and IoT lifecycle phases. Specifically, we provide a fresh look at energysustainable IoT and identify energy provision, transfer, and energy efficiency as the three main energyrelated processes whose harmonious coexistence pushes toward realizing self-sustainable IoT systems. Their main related technologies, recent advances, challenges, and research directions are also discussed. Moreover, we overview relevant performance metrics to assess the energy-sustainability potential of a certain technique, technology, device, or network, together with target values for the next generation of wireless systems, and discuss protocol, integration, and implementation issues. Overall, this paper offers insights that are valuable for advancing sustainability goals for present and future generations.

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“…Long short-term memory (LSTM) networks can use their architectural strengths to retain long-term memories of bearing conditions. LSTM networks effectively address the limitations and instability issues associated with predicting the RUL of rolling bearings, producing superior forecasting performance [ 33 , 34 , 35 , 36 ]. The LSTM method demands greater memory to handle time sequences.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rotating Machinery Using an Optimal Blind Deconvolution Method and Hybrid Invertible Neural Network

Gao,

Ahmad,

Kim

2024

Sensors

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This paper proposes a novel approach to predicting the useful life of rotating machinery and making fault diagnoses using an optimal blind deconvolution and hybrid invertible neural network. First, a new optimal adaptive maximum second-order cyclostationarity blind deconvolution (OACYCBD) is developed for denoising vibration signals obtained from rotating machinery. This technique is obtained from the optimization of traditional adaptive maximum second-order cyclostationarity blind deconvolution (ACYCBD). To optimize the weights of conventional ACYCBD, the proposed method utilizes a probability density function (PDF) of Monte Carlo to assess fault-related incipient changes in the vibration signal. Cross-entropy is used as a convergence criterion for denoising. Because the denoised signal carries information related to the health of the rotating machinery, a novel health index is calculated in the second step using the peak value and square of the arithmetic mean of the signal. The novel health index can change according to the degradation of the health state of the rotating bearing. To predict the remaining useful life of the bearing in the final step, the health index is used as input for a newly developed hybrid invertible neural network (HINN), which combines an invertible neural network and long short-term memory (LSTM) to forecast trends in bearing degradation. The proposed approach outperforms SVM, CNN, and LSTM methods in predicting the remaining useful life of bearings, showcasing RMSE values of 0.799, 0.593, 0.53, and 0.485, respectively, when applied to a real-world industrial bearing dataset.

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Anomaly Detection Approach in Industrial Control Systems Based on Measurement Data

Cited by 7 publications

References 27 publications

An Anomaly Detection Approach to Determine Optimal Cutting Time in Cheese Formation

An Anomaly Detection Approach to Determine Optimal Cutting Time in Cheese Formation

Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions

Fault Diagnosis of Rotating Machinery Using an Optimal Blind Deconvolution Method and Hybrid Invertible Neural Network

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