Transformer Data Analysis for Predictive Maintenance

Putchala, Sreshta R.; Kotha, Rithik; Guda, Vanitha; Ramadevi, Y.

doi:10.1007/978-981-16-7389-4_21

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…For the inspection, assuming a 5 m distance to the transformer, the cameras achieved sampling distances of (RGB) 1.6 mm/px, (ToF/Depth) 10.3 mm/px and (InfraRed (IR)/Thermal) 4.6 mm/px. The choice of sensors is based on the common targets of onsite transformer inspections [6] (visible abnormalities, such as oil spills, cracks, and bulging, and the occurrence of unusual sounds) and the correlation of transformer failures to certain weather conditions [8] (especially, lightning and the highest temperature reached). Sensor data are stored in the backend, after each inspection, using standard formats.…”

Section: Proposed Systemmentioning

confidence: 99%

“…Therefore, in transformer fleet management, it is crucial to properly maintain and monitor the condition of transformers according to their load operation levels to minimize failure rates, extend the lifetime of the transformers, and allow for their timely retirement. The use of continuous monitoring and predictive maintenance is essential to prolong the life of transmission systems and reduce any unexpected outages [3][4][5][6][7][8]. Standard monitoring solutions, such as those presented in [9,10], are integrated into the Supervisory Control and Data Acquisition (SCADA) systems to monitor the Key Performance Indicators (KPIs) of power transformers.…”

Section: Introductionmentioning

confidence: 99%

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First Measurement Campaign by a Multi-Sensor Robot for the Lifecycle Monitoring of Transformers

Waikat,

Jelidi,

Lic

et al. 2024

Energies

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Transformers are a very important asset in the electrical transmission grid, and they can suffer from destructive events—e.g., rare transformer fires. Unfortunately, destructive events often lead to a lack of data available for investigators during post-event forensics and failure analysis. This fact has motivated our design and implementation of a robotic multi-sensor platform and cloud backend solution for the lifecycle monitoring, inspection, diagnostics, and condition assessment of transformers. The robotic platform collects data from specific viewpoints around the transformer during operation and at specific relevant lifecycle milestones of the transformer (e.g., at the factory acceptance test) in an automated, repetitive, precise, and reliable manner. The acquired data are stored in the cloud backend, which also provides computing resources and data access to relevant in- and off-premises services (e.g., respectively, SCADA systems, and weather reports). In this paper, we present the results of our first measurement campaign to showcase the value of our solution for transformer lifecycle monitoring, for anomaly detection, and as a crucial tool for post-event forensics in the case of destructive events.

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Section: Proposed Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

First Measurement Campaign by a Multi-Sensor Robot for the Lifecycle Monitoring of Transformers

Waikat,

Jelidi,

Lic

et al. 2024

Energies

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“…The time series analysis forms a fundamental aspect of predictive maintenance using classical statistical methods. In this context, many works have explored various time series models, including Autoregressive Integrated Moving Average (ARIMA) [15][16][17], Exponential Smoothing (ES) [18,19], and Stationary Autoregressive Integrated Moving Average (SARIMA) [20,21]. These models capture patterns and trends within historical data, enabling accurate forecasting of future equipment failures, and have been applied in different industry environments.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Classical Statistical Methods for Sustainable Maintenance in Automotive Assembly Equipment

Bucay-Valdiviezo,

Escudero-Villa,

Paredes-Fierro

et al. 2023

Sustainability

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Predictive maintenance management plays a crucial role in ensuring the reliable operation of equipment in industry. While continuous monitoring technology is available today, equipment without sensors limits continuous equipment state data recording. Predictive maintenance has been effectively carried out using artificial intelligence algorithms for datasets with sufficient data. However, replicating these results with limited data is challenging. This work proposes the use of time series models to implement predictive maintenance in the equipment of an automotive assembly company with few records available. For this purpose, three models are explored—Holt–Winters Exponential Smoothing (HWES), Autoregressive Integrated Moving Average (ARIMA), and Seasonal Autoregressive Integrated Moving Average (SARIMA)—to determine the most accurate forecasting of future equipment downtime and advocate the use of SAP PM for effective maintenance process management. The data were obtained from five equipment families from January 2020 to December 2022, representing 36 registers for each piece of equipment. After data fitting and forecasting, the results indicate that the SARIMA model best fits seasonal characteristics, and the forecasting offers valuable information to help in decision-making to avoid equipment downtime, despite having the highest error. The results were less favorable when handling datasets with random components, requiring model recalibration for short-term forecasting.

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