Advancing from Predictive Maintenance to Intelligent Maintenance with AI and IIoT

Zheng, Haining; Paiva, António R. C.; Gurciullo, Christopher S.

doi:10.48550/arxiv.2009.00351

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…AI technologies such as artificial neural networks (ANNs) and genetic algorithms (GAs) can be used to optimize energy and resource consumption [16][17][18][19]. Predictive maintenance enabled by IIoT and machine learning has significant potential to reduce waste and improve productivity [20][21][22][23][24][25][26]. Several studies have reported the successful implementation of AI techniques to optimize fuel usage and improve efficiency in various applications.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Sasikumar et al [24] used AI and IIoT for a sustainable smart industry, improving security and energy efficiency. Zheng et al [25] applied AI to analyze data from gensets and equipment to predict failures and maintenance needs, with IIoT enabling real-time monitoring and control of gensets for efficient maintenance. Ramesh et al [26] reported cost savings and improved availability when implementing predictive maintenance systems in remote plants.…”

Section: Literature Reviewmentioning

confidence: 99%

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Revolutionizing IC Genset Operations with IIoT and AI: A Study on Fuel Savings and Predictive Maintenance

et al. 2023

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In a world increasingly aware of its carbon footprint, the quest for sustainable energy production and consumption has never been more urgent. A key player in this monumental endeavor is fuel conservation, which helps curb greenhouse gas emissions and preserve our planet’s finite resources. In the realm of the Industrial Internet of Things (IIoT) and artificial intelligence (AI) technologies, Caterpillar (CAT) generator set (genset) operations have been revolutionized, unlocking unprecedented fuel savings and reducing environmental harm. Envision a system that not only enhances fuel efficiency but also anticipates maintenance needs with state-of-the-art technology. This standalone IIoT platform crafted with Visual Basic.Net (VB.Net) and the KEPware Object linking and embedding for Process Control (OPC) server gathers, stores, and analyzes data from CAT gensets, painting a comprehensive picture of their inner workings. By leveraging the Modbus Remote Terminal Unit (RTU) protocol, the platform acquires vital parameters such as engine load, temperature, pressure, revolutions per minute (RPM), and fuel consumption measurements, from a radar transmitter. However, the magic does not stop there. Machine Learning.Net (ML.Net) empowers the platform with machine learning capabilities, scrutinizing the generator’s performance over time, identifying patterns and forecasting future behavior. Equipped with these insights, the platform fine tunes its operations, elevates fuel efficiency, and conducts predictive maintenance, minimizing downtime and amplifying overall efficiency. The evidence is compelling: IIoT and AI technologies have the power to yield substantial fuel savings and enhance performance through predictive maintenance. This research offers a tangible solution for industries eager to optimize operations and elevate efficiency by embracing IIoT and AI technologies in CAT genset operations. The future is greener and smarter, and it starts now.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Revolutionizing IC Genset Operations with IIoT and AI: A Study on Fuel Savings and Predictive Maintenance

et al. 2023

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“…The added value of CI/CD pipelines is achieved through automation, but it is even possible to perform each CI/CD process step manually [50]. The CI/CD automation keeps the deployed ML models up to date without causing disruptions to production (Figure 4, Deployment) [51].…”

Section: Implementation Of Software Sensor and Machine-learning Model...mentioning

confidence: 99%

Edge-Computing and Machine-Learning-Based Framework for Software Sensor Development

Hanzelik

Kummer

Abonyi

2022

Sensors

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The present research presents a framework that supports the development and operation of machine-learning (ML) algorithms to develop, maintain and manage the whole lifecycle of modeling software sensors related to complex chemical processes. Our motivation is to take advantage of ML and edge computing and offer innovative solutions to the chemical industry for difficult-to-measure laboratory variables. The purpose of software sensor models is to continuously forecast the quality of products to achieve effective quality control, maintain the stable production condition of plants, and support efficient, environmentally friendly, and harmless laboratory work. As a result of the literature review, quite a few ML models have been developed in recent years that support the quality assurance of different types of materials. However, the problems of continuous operation, maintenance and version control of these models have not yet been solved. The method uses ML algorithms and takes advantage of cloud services in an enterprise environment. Industrial 4.0 devices such as the Internet of Things (IoT), edge computing, cloud computing, ML, and artificial intelligence (AI) are core techniques. The article outlines an information system structure and the related methodology based on data from a quality-assurance laboratory. During the development, we encountered several challenges resulting from the continuous development of ML models and the tuning of their parameters. The article discusses the development, version control, validation, lifecycle, and maintenance of ML models and a case study. The developed framework can continuously monitor the performance of the models and increase the amount of data that make up the models. As a result, the most accurate, data-driven and up-to-date models are always available to quality-assurance engineers with this solution.

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Optimizing biodiesel production from waste with computational chemistry, machine learning and policy insights: a review

Osman,

Nasr,

Farghali

et al. 2024

Environ Chem Lett

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The excessive reliance on fossil fuels has resulted in an energy crisis, environmental pollution, and health problems, calling for alternative fuels such as biodiesel. Here, we review computational chemistry and machine learning for optimizing biodiesel production from waste. This article presents computational and machine learning techniques, biodiesel characteristics, transesterification, waste materials, and policies encouraging biodiesel production from waste. Computational techniques are applied to catalyst design and deactivation, reaction and reactor optimization, stability assessment, waste feedstock analysis, process scale-up, reaction mechanims, and molecular dynamics simulation. Waste feedstock comprise cooking oil, animal fat, vegetable oil, algae, fish waste, municipal solid waste and sewage sludge. Waste cooking oil represents about 10% of global biodiesel production, and restaurants alone produce over 1,000,000 m3 of waste vegetable oil annual. Microalgae produces 250 times more oil per acre than soybeans and 7–31 times more oil than palm oil. Transesterification of food waste lipids can produce biodiesel with a 100% yield. Sewage sludge represents a significant biomass waste that can contribute to renewable energy production.

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Advancing from Predictive Maintenance to Intelligent Maintenance with AI and IIoT

Cited by 3 publications

References 5 publications

Revolutionizing IC Genset Operations with IIoT and AI: A Study on Fuel Savings and Predictive Maintenance

Revolutionizing IC Genset Operations with IIoT and AI: A Study on Fuel Savings and Predictive Maintenance

Edge-Computing and Machine-Learning-Based Framework for Software Sensor Development

Optimizing biodiesel production from waste with computational chemistry, machine learning and policy insights: a review

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