Fault detective: Automatic fault-detection for solar thermal systems based on artificial intelligence

Feierl, Lukas; Unterberger, Viktor; Rossi, Claudio; Geradts, Bernhard; Gaetani, Manuel

doi:10.1016/j.seja.2023.100033

Cited by 4 publications

(5 citation statements)

References 19 publications

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“…Common examples of preprocessing include regularization, which transforms the data to equidistant timestamps, and interpolation, dealing with missing data. This might help to simplify algorithms or reduce unexpected behavior, while some algorithms even require specific data properties like equidistant timestamps [25]. A list of pre-processing algorithms can be found in Table 15.…”

Section: Preprocessing Algorithmsmentioning

confidence: 99%

Efficient Gathering, Storing, Distributing and Validation of Data

Ramschak

2024

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Section: Preprocessing Algorithmsmentioning

confidence: 99%

Efficient Gathering, Storing, Distributing and Validation of Data

Ramschak

2024

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“…The main challenge with anomalies is the potentially high number of false alarms due to the complex nature of the plant behavior and the large number of sensors that could be tracked. 2,3…”

Section: Feature Articlementioning

confidence: 99%

“…In addition, the automatic algorithms for the solar-thermal domain are much more prone to false alarms. 2,3 Design Studies Applications to support fault detection have also been studied in various other domains. An overview is shown in Figure 5.…”

Section: Photovoltaic Domainmentioning

confidence: 99%

“…› SunSearcher -Early version of a machine learning algorithm called Fault-Detective. 3 It automatically identifies correlated sensor measurements using recursive feature selection and then models the relations using random forest regression. Multiple algorithms are used to track all sensor measurements.…”

Section: Implementation Detailsmentioning

confidence: 99%

“…Second, feedback to SunScreen is mixed up with feedback to the anomaly detection algorithm SunSearcher. 3 In hindsight, a visual framework focusing on manual fault detection would have been easier to validate. Nevertheless, it was a strategic choice of the stakeholders to opt for automatic fault detection to benefit from the automation.…”

Section: Feature Articlementioning

confidence: 99%

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SunScreen: Visual Fault Detection for Solar-Thermal Systems

Feierl,

Möller,

Luidolt

2023

IEEE Comput. Grap. Appl.

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Fault detection is essential to ensure the proper operation of solar-thermal plants. Hence, monitoring personnel frequently analyze the data to detect unusual behavior. While visualization approaches may considerably support the monitoring personnel during their work, no existing application can yet deal with the multivariate and time-dependent sensor data, or does not fully support the users' workflow. Thus, this work introduces the visual framework SunScreen. It allows users to explore the sensor data, automatically detected anomalies, and system events (e.g., already detected faults and services). The feedback from the users shows that they appreciate the tool and especially its annotation functionality. However, the SUS results indicate that it does not meet all requirements yet. S olar-thermal plants use solar irradiation to produce heat. With heat accounting for approximately 50% of the global energy demand, 1 this technology could play a crucial role in the transition to renewable energy. However, monitoring is essential to ensure the proper operation of the plants. Thus, monitoring personnel frequently analyze the sensor data to detect unusual behavior.However, the main challenge for this fault detection is the complex behavior of solar-thermal plants and its multidimensional, time-dependent, and non-linear measurement data. As a result, the data is hard to interpret even by experts, while automatic fault detection algorithms are prone to raise false alarms. 2,3 Visual frameworks may considerably support the monitoring personnel by combining domain knowledge and automatic anomaly detection and letting users explore the measurement data.Unfortunately, no designated software for fault detection at solar thermal plants exists yet. Lacking an alternative, operators often use supervisory control and data acquisition (SCADA) applications. However, mainly targeted at industrial processes, they do not support analyzing the multidimensional sensor data and provide little functionality for exploring automatic fault detection results. While applications related to XXXX-XXX © 2023 IEEE Digital Object Identifier 10.1109/XXX.0000.0000000

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Unsupervised fault detection in automated sequential manufacturing processes through image analysis and convolutional LSTM-based next visual status prediction

Yu,

Baek

2024

Int J Adv Manuf Technol

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Fault detective: Automatic fault-detection for solar thermal systems based on artificial intelligence

Cited by 4 publications

References 19 publications

Efficient Gathering, Storing, Distributing and Validation of Data

Efficient Gathering, Storing, Distributing and Validation of Data

SunScreen: Visual Fault Detection for Solar-Thermal Systems

Unsupervised fault detection in automated sequential manufacturing processes through image analysis and convolutional LSTM-based next visual status prediction

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