Development of Predictive Maintenance Interface Using Multiple Linear Regression

Abbasi, Tayaba; Lim, King Hann; Rosli, Nurfatihah Syalwiah; Ismail, Idris; Ibrahim, Rosdiazli

doi:10.1109/icias.2018.8540602

Cited by 13 publications

(7 citation statements)

References 10 publications

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“…Based on the overall network architecture level, the integrated management platform of the Internet of Things obtains the device health status monitoring signal with the help of sensor nodes and obtains the operation parameter information. The network layer is uploaded to the equipment health monitoring network of the Internet of Things management platform, and then the data is further transmitted to the application layer for monitoring signal analysis and fault feature extraction to realize fault diagnosis and prediction [3]. The equipment health monitoring platform of the Internet of Things has various functions such as data collection and analysis, data visualization processing, equipment maintenance, fault diagnosis, and early warning.…”

Section: Technical Support System Based On the Internet Of Thingsmentioning

confidence: 99%

The Application of Data Mining Technology in the Predictive Maintenance for Oil and Gas Equipment

2022

AJETS

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Data mining is a new research topic in intelligent predictive maintenance by searching, counting, and processing a large amount of data to obtain valuable information. With the rapid development of the petroleum industry in recent years, automation and intelligent oil and gas equipment have been constantly updated and applied with a significant role in the daily production scheduling process control in the oil and gas fields. In this context, this work first discussed the basic concept of data mining technology and predictive maintenance, then presented the implementation route of data mining technology in predictive maintenance, and finally proposed the predictive maintenance scheme of deep hole drilling tools based on the two-mode algorithm. This study mainly discussed the basic problems and key technologies of data mining technology in the predictive maintenance of oil and gas equipment, aiming to provide new solutions to the equipment fault prediction.

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Section: Technical Support System Based On the Internet Of Thingsmentioning

confidence: 99%

The Application of Data Mining Technology in the Predictive Maintenance for Oil and Gas Equipment

2022

AJETS

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“…Various machine learning approaches for clustering, classification and regression are widely applied for PM and proven to be effective [14,[18][19][20]. This includes different PM applications, such as the calculation of the Remaining Useful Life (RUL) [7,21] or condition monitoring to prevent premature failures [3].…”

Section: Related Workmentioning

confidence: 99%

Integrating Predictive Maintenance in Adaptive Process Scheduling for a Safe and Efficient Industrial Process

Görür

Sivrikaya³

2021

Applied Sciences

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Predictive maintenance (PM) algorithms are widely applied for detecting operational anomalies on industrial processes to schedule for a maintenance intervention before a possible breakdown; however, much less focus has been devoted to the use of such prognostics in process scheduling. The existing solutions mostly integrate preventive approaches to protect the machines, usually causing downtimes. The premise of this study is to develop a process scheduling mechanism that selects an acceptable operating condition for an industrial process to adapt to the predicted anomalies. As PM is largely a data-driven approach (hence, it relies on the setup), we first compare different PM approaches and identify a one-class support vector machine (OCSVM) as the best performing option for the anomaly detection on our setup. Then, we propose a novel pipeline to integrate maintenance predictions into a real-time, adaptive process scheduling mechanism. According to the abnormal readings, it schedules for the most suitable operation, i.e., optimizing for machine health and process efficiency, toward preventing breakdowns while maintaining its availability and operational state, thereby reducing downtimes. To demonstrate the pipeline on the action, we implement our approach on a small-scale conveyor belt, utilizing our Internet of Things (IoT) framework. The results show that our PM-based adaptive process control retains an efficient process under abnormal conditions with less or no downtime. We also conclude that a PM approach does not provide sufficient efficiency without its integration into an autonomous planning process.

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“…Clearly multiclass systems provide more knowledge about the state of the system, but are also more complex. Regression models instead are devoted to learn the behavior of the system, in order to possibly predict its future states [13].…”

Section: Modelsmentioning

confidence: 99%

Anomaly Detection and Classification in Predictive Maintenance Tasks with Zero Initial Training

Morselli¹,

Bedogni

Mirani

et al. 2021

IoT

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The Fourth Industrial Revolution has led to the adoption of novel technologies and methodologies in factories, making these more efficient and productive. Among the new services which are changing industry, there are those based on machine learning algorithms, which enable machines to learn from their past observations and hence possibly forecast future states. Specifically, predictive maintenance represents the opportunity to understand in advance possible machine outages due to broken parts and schedule the necessary maintenance operations. However, in real scenarios predictive maintenance struggles to be adopted due to a multitude of variables and the heavy customization it requires. In this work, we propose a novel framework for predictive maintenance, which is trained online to recognize new issues reported by the operators. Our framework, tested on different scenarios and with a varying number and several kinds of sensors, shows recall levels above 0.85, demonstrating its effectiveness and adaptability.

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Development of Predictive Maintenance Interface Using Multiple Linear Regression

Cited by 13 publications

References 10 publications

The Application of Data Mining Technology in the Predictive Maintenance for Oil and Gas Equipment

The Application of Data Mining Technology in the Predictive Maintenance for Oil and Gas Equipment

Integrating Predictive Maintenance in Adaptive Process Scheduling for a Safe and Efficient Industrial Process

Anomaly Detection and Classification in Predictive Maintenance Tasks with Zero Initial Training

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