Modeling System Based on Machine Learning Approaches for Predictive Maintenance Applications

Martins, João Pedro Serrasqueiro; Rodrigues, Filipe Martins; Henriques, Nuno Paulo Ferreira

doi:10.18502/keg.v5i6.7105

Cited by 7 publications

(4 citation statements)

References 25 publications

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“…According to Nacchia et al (2021) [20], PdM has shown great potential when guided by an ML algorithm. Several studies have corroborated this statement, such as those by Zhang and Yang (2019) [18]; Chen et al (2020a) [19]; Nentwich, Junker, and Reinhart (2020) [21]; Martins, Rodriguez, and Henriques (2020) [22]; Welte, Estler, and Lucke (2020) [23]; and Theissler et al (2021) [17]. Thus, the next section presents the relevant fundamentals of ML for PdM applications.…”

mentioning

confidence: 76%

Machine Learning for the Detection and Diagnosis of Anomalies in Applications Driven by Electric Motors

Ferraz Júnior,

Romero,

Hsieh

2023

Sensors

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Manufacturing systems are becoming increasingly flexible, necessitating the adoption of new technologies that allow adaptations to a turbulent and complex modern market. Consequently, modern concepts of production systems require horizontal and vertical integration, extending across value networks and within a factory or production shop. The integration of these environments enables the acquisition of a substantial amount of data containing information pertaining to production, processes, and equipment located on the shop floor. When these data and information are processed and analyzed, they have the potential to reveal valuable insights and knowledge about the manufacturing systems, offering interpretive outcomes for strategic decision making. One of the opportunities presented in this context includes the implementation of predictive maintenance (PdM). However, industrial adoption of PdM is still relatively low. In this paper, the aim is to propose a methodology for selecting the main attributes (variables) to be considered in the instrumentation setup of rotating machines driven by electric motors to decrease the associated costs and the time spent defining them. For this, the most well-known data science and machine learning algorithms are investigated to choose the one most adequate for this task. For the experiments, different testing scenarios were proposed to detect the different possible types of anomalies, such as uncoupled, overloaded, unbalanced, misaligned, and normal. The results obtained show how these algorithms can be effective in classifying the different types of anomalies and that the two models that presented the best accuracy values were k-nearest neighbor and multi-layer perceptron.

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mentioning

confidence: 76%

Machine Learning for the Detection and Diagnosis of Anomalies in Applications Driven by Electric Motors

Ferraz Júnior,

Romero,

Hsieh

2023

Sensors

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“…Additionally, big data platforms are employed for real-time predictive assessment of oscillatory stability, utilizing techniques like recurrent neural networks and leveraging records from tools like WAProtector [13]. The effective functioning of smart electricity grids hinges on acquiring, analyzing, and processing the enormous volume of data generated by various components, including smart sensors, individual smart meters, and environmental data collection devices like solar radiation sensors and wind-speed meters [14]. Addressing the challenges posed by the sheer scale of this data necessitates the adoption of cutting-edge data analytics, comprehensive big data management strategies, and robust monitoring methodologies.…”

Section: Big Data Analytics In Electric Grid Systemsmentioning

confidence: 99%

Intelligent Electric Grid Maintenance via an Adaptive Predictive Maintenance Optimization Algorithm

Kiran Thatikonda

2024

jes

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This study presents a novel framework for the predictive maintenance of electric grid infrastructure, leveraging an Adaptive Predictive Maintenance Optimization (APMO) algorithm. Our comprehensive system architecture integrates IoT sensors and drone surveillance, harnessing grid computing for data processing and machine learning for analytics. The APMO algorithm, underpinned by reinforcement learning, dynamically refines maintenance schedules, enhancing operational efficiency. Results from simulated sensor data exhibit a negligible correlation, indicating a multi-faceted approach to predictive analytics. Specifically, the Health Index distribution showed a wide range of 0.7 to 1.0, while the electrical current maintained a stable mean at 10 A. Further, the APMO algorithm demonstrated a promising improvement in maintenance efficiency from 40% to 75% over a year. This research introduces a scalable, robust system for grid management, paving the way for smarter infrastructure maintenance.

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“…A case study was presented as an example, showcasing a predictive model capable of anticipating unexpected machine failures. Martins et al [9], implemented an automatic forecasting model in a test bench to identify machine failures and contribute to the advancement of algorithms for preventive and descriptive maintenance. This implementation aims to improve the recognition of machine failures and facilitate the development of maintenance strategies that are both preventive and descriptive in nature.…”

Section: Related Workmentioning

confidence: 99%

An IoT and Machine Learning-Based Predictive Maintenance System for Electrical Motors

Mohammed,

Abdulateef,

Hamad

2023

JESA

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The rise of Industry 4.0 and smart manufacturing has highlighted the importance of utilizing intelligent manufacturing techniques, tools, and methods, including predictive maintenance. This feature allows for the early identification of potential issues with machinery, preventing them from reaching critical stages. This paper proposes an intelligent predictive maintenance system for industrial equipment monitoring. The system integrates Industrial IoT, MQTT messaging and machine learning algorithms. Vibration, current and temperature sensors collect real-time data from electrical motors which is analyzed using five ML models to detect anomalies and predict failures, enabling proactive maintenance. The MQTT protocol is used for efficient communication between the sensors, gateway devices, and the cloud server. The system was tested on an operational motors dataset, five machine learning algorithms, namely k-nearest neighbor (KNN), supported vector machine (SVM), random forest (RF), linear regression (LR), and naive bayes (NB), are used to analyze and process the collected data to predict motor failures and offer maintenance recommendations. Results demonstrate the random forest model achieves the highest accuracy in failure prediction. The solution minimizes downtime and costs through optimized maintenance schedules and decisions. It represents an Industry 4.0 approach to sustainable smart manufacturing.

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Modeling System Based on Machine Learning Approaches for Predictive Maintenance Applications

Cited by 7 publications

References 25 publications

Machine Learning for the Detection and Diagnosis of Anomalies in Applications Driven by Electric Motors

Machine Learning for the Detection and Diagnosis of Anomalies in Applications Driven by Electric Motors

Intelligent Electric Grid Maintenance via an Adaptive Predictive Maintenance Optimization Algorithm

An IoT and Machine Learning-Based Predictive Maintenance System for Electrical Motors

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