Relaxed Rule-Based Learning for Automated Predictive Maintenance: Proof of Concept

Razgon, Margarita; Mousavi, Alireza

doi:10.3390/a13090219

Cited by 6 publications

(3 citation statements)

References 42 publications

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“…The gene sequencing (DNA of the process) can be a powerful tool for interpreting and demonstrating the knowledge graphs in CDT. Another potential area to explore is the role of GIP in simplifying and relaxing rule-based models (e.g., [45] and [46]).…”

Section: Discussionmentioning

confidence: 99%

The Genomics of Industrial Process Through the Qualia of Markovian Behavior

Danishvar

Mousavi

Daneshvar

2022

IEEE Trans. Syst. Man Cybern, Syst.

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A technique for registering and relating events that cause an observable and definable system state is proposed. Discrete events of system-state transfer are expressed by event tracking and clustering in the form of contiguous quanta of data. This approach is capable of describing typical processes in industrial systems in a chain of codes that contain system input/output parameters. The constituent nodes of the Markovian Processes chain form a series akin to genes in the deoxyribonucleic acid, repeatable and predictable. The process genes are the quanta of information that aligns to represent a chain of activities (process). They describe the causal links between occurring events forming a pattern (pathway) that leads to a well-specified output (e.g., a product with a defect or otherwise). The creation of process genomics requires the knowledge of system observed or latent parameters (state) as well as the state change at specified time intervals (discretization). The process genomics theory is tested in an industrial case study for quality assessment and control of glue dispensing in micro-semiconductor manufacturing. The resulting definitions of the system state and interrelationship of control parameters contribute to the development of the process genes. The outcome of the gene alignment is the geometric interpretation of the glue droplet formation. A predicted or observed droplet within the production tolerance leads to a nondefective product. The principle of creating production genomics is to find and rectify the defect-causing genes or to disrupt the sequences that lead to producing defective products, leading to a zero-defect manufacturing process.

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Section: Discussionmentioning

confidence: 99%

The Genomics of Industrial Process Through the Qualia of Markovian Behavior

Danishvar

Mousavi

Daneshvar

2022

IEEE Trans. Syst. Man Cybern, Syst.

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“…Another potential area to explore is the role of GIP in simplifying and relaxing rulebased models (e.g. [48], [49]).…”

Section: VImentioning

confidence: 99%

The Genomics of Industrial Process through the Qualia of Markovian Behaviour

Danishvar¹,

Daneshvar²,

Mousavi³

2022

Preprint

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A technique for registering and relating events that cause an observable and definable system state is proposed. Discrete events of system state transfer are expressed by event tracking and clustering in the form of contiguous quanta of data. This approach is capable of describing typical processes in industrial systems in a chain of codes that contain system input/output parameters. The constituent nodes of the Markovian Processes chain form a series akin to genes in the DNA, repeatable and predictable.<br>

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“…The advantage of using a rule-based policy on a deep neural network is for further refinement such that the rules can be generated for the fog nodes that belong to the item set, which lowers the time complexity to help in the prediction of the failure of the insufficient resource. To determine accurate prediction, it is important to create rules based on relationships between the cause and type of insufficiency of resources of machine failure [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Proactive Fault Prediction of Fog Devices Using LSTM-CRP Conceptual Framework for IoT Applications

Haneef

Neelanarayanan

2023

Sensors

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Technology plays a significant role in our daily lives as real-time applications and services such as video surveillance systems and the Internet of Things (IoT) are rapidly developing. With the introduction of fog computing, a large amount of processing has been done by fog devices for IoT applications. However, a fog device’s reliability may be affected by insufficient resources at fog nodes, which may fail to process the IoT applications. There are obvious maintenance challenges associated with many read-write operations and hazardous edge environments. To increase reliability, scalable fault-predictive proactive methods are needed that predict the failure of inadequate resources of fog devices. In this paper, a Recurrent Neural Network (RNN)-based method to predict proactive faults in the event of insufficient resources in fog devices based on a conceptual Long Short-Term Memory (LSTM) and novel Computation Memory and Power (CRP) rule-based network policy is proposed. To identify the precise cause of failure due to inadequate resources, the proposed CRP is built upon the LSTM network. As part of the conceptual framework proposed, fault detectors and fault monitors prevent the outage of fog nodes while providing services to IoT applications. The results show that the LSTM along with the CRP network policy method achieves a prediction accuracy of 95.16% on the training data and a 98.69% accuracy on the testing data, which significantly outperforms the performance of existing machine learning and deep learning techniques. Furthermore, the presented method predicts proactive faults with a normalized root mean square error of 0.017, providing an accurate prediction of fog node failure. The proposed framework experiments show a significant improvement in the prediction of inaccurate resources of fog nodes by having a minimum delay, low processing time, improved accuracy, and the failure rate of prediction was faster in comparison to traditional LSTM, Support Vector Machines (SVM), and Logistic Regression.

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Relaxed Rule-Based Learning for Automated Predictive Maintenance: Proof of Concept

Cited by 6 publications

References 42 publications

The Genomics of Industrial Process Through the Qualia of Markovian Behavior

The Genomics of Industrial Process Through the Qualia of Markovian Behavior

The Genomics of Industrial Process through the Qualia of Markovian Behaviour

Proactive Fault Prediction of Fog Devices Using LSTM-CRP Conceptual Framework for IoT Applications

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