Machine Fault Detection Using a Hybrid CNN-LSTM Attention-Based Model

Borrè, A; Seman, Laio Oriel; Camponogara, Eduardo; Stefenon, Stéfano Frizzo; Mariani, Viviana Cocco; Coelho, Leandro dos Santos

doi:10.3390/s23094512

Cited by 32 publications

(5 citation statements)

References 74 publications

(69 reference statements)

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“…Applying the link between numerous linear and non-linear process factors is an important task to increase the accuracy of AI models [162]. Since the data amount depends on the quality of the calibration and hyper-parameter optimizations [163], the comparison difficulty concerning the performance varies according to the methodology and objectives used to reach data-driven models and can be improved with the advancement of deep analytical techniques and Internet of Things integration [164][165][166].…”

Section: Artificial Intelligence Applicationsmentioning

confidence: 99%

A Review of Automation and Sensors: Parameter Control of Thermal Treatments for Electrical Power Generation

Buratto,

Muniz,

Nied

et al. 2024

Sensors

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This review delves into the critical role of automation and sensor technologies in optimizing parameters for thermal treatments within electrical power generation. The demand for efficient and sustainable power generation has led to a significant reliance on thermal treatments in power plants. However, ensuring precise control over these treatments remains challenging, necessitating the integration of advanced automation and sensor systems. This paper evaluates the pivotal aspects of automation, emphasizing its capacity to streamline operations, enhance safety, and optimize energy efficiency in thermal treatment processes. Additionally, it highlights the indispensable role of sensors in monitoring and regulating crucial parameters, such as temperature, pressure, and flow rates. These sensors enable real-time data acquisition, facilitating immediate adjustments to maintain optimal operating conditions and prevent system failures. It explores the recent technological advancements, including machine learning algorithms and IoT integration, which have revolutionized automation and sensor capabilities in thermal treatment control. Incorporating these innovations has significantly improved the precision and adaptability of control systems, resulting in heightened performance and reduced environmental impact. This review underscores the imperative nature of automation and sensor technologies in thermal treatments for electrical power generation, emphasizing their pivotal role in enhancing operational efficiency, ensuring reliability, and advancing sustainability in power generation processes.

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Section: Artificial Intelligence Applicationsmentioning

confidence: 99%

A Review of Automation and Sensors: Parameter Control of Thermal Treatments for Electrical Power Generation

Buratto,

Muniz,

Nied

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Hybrid models: Recent advancements have explored hybrid models that combine multiple DL architectures to leverage their individual strengths [50,51]. For instance, combining CNNs with LSTMs allows the model to capture both spatial and temporal features, improving the robustness [52,53]. Similarly, integrating VAEs with GANs can enhance the model's ability to generate realistic data and detect anomalies based on reconstruction errors and adversarial loss, particularly on time series data [54].…”

Section: Attention Mechanisms and Transformersmentioning

confidence: 99%

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

Loddo,

Ghiani,

Perniciano

et al. 2024

Information

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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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“…The study presented by Shen et al [48] delves into the utilization of deep reinforcement learning algorithms to enhance the energy efficiency of hydrogen fuel cell buses by dynamically adjusting their velocity profiles. As well evaluated nowadays, the energy supply has become more efficient with the use of time series forecasting [49] using hybrid models [50], classification (computer vision [51], convolutional neural networks [52], deep neural networks [53], multilayer perceptron, and k-nearest neighbors [54]), and Internet of Things (IoT) using embedded systems [55].…”

Section: Technology and Efficiency Improvementmentioning

confidence: 99%

The Sustainability Concept: A Review Focusing on Energy

Muniz,

da Costa Júnior,

Buratto

et al. 2023

Sustainability

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The concept of sustainability, with a focus on energy, has emerged as a central tenet in addressing the mounting global challenges of environmental degradation and resource depletion. Indicators of sustainability focusing on energy are crucial tools used to assess and monitor progress toward achieving a more sustainable energy system. These indicators provide valuable insights into the environmental, social, and economic dimensions of energy practices and their long-term impacts. By analyzing and understanding these indicators, policymakers, businesses, and communities can make informed decisions, formulate effective policies, and steer their efforts toward a more sustainable energy future. These indicators serve as navigational guides, steering the world toward energy practices that support both present needs and the well-being of future generations. In this paper, the concept of sustainability and measurement indexes used are reviewed, focusing on energy factors. The focus of the discussion presented here is related to an assessment of the possibilities for improving energy efficiency and evaluating the indicators that are used to measure whether the desired levels of sustainability are being achieved.

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Machine Fault Detection Using a Hybrid CNN-LSTM Attention-Based Model

Cited by 32 publications

References 74 publications

A Review of Automation and Sensors: Parameter Control of Thermal Treatments for Electrical Power Generation

A Review of Automation and Sensors: Parameter Control of Thermal Treatments for Electrical Power Generation

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

The Sustainability Concept: A Review Focusing on Energy

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