<i>SmartConnector</i>: A Self-Powered IoT Solution to Ease Predictive Maintenance in Substations

Kadechkar, Akash; Riba, Jordi-Roger; Moreno-Eguilaz, Manuel; Pérez, Joan

doi:10.1109/jsen.2020.2998157

Cited by 32 publications

(24 citation statements)

References 19 publications

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“…This context supported the rise of predictive maintenance strategies, which is backed up by some recent works [ 5 , 13 , 14 , 15 , 16 , 17 ] in the literature proposing Internet of Things (IoT) solutions to address such challenges. In this way, edge computing is also emerging as a technology in several aspects of IIoT [ 18 ].…”

Section: Introductionmentioning

confidence: 79%

TIP4.0: Industrial Internet of Things Platform for Predictive Maintenance

Resende

Folgado

Oliveira

et al. 2021

Sensors

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Industry 4.0, allied with the growth and democratization of Artificial Intelligence (AI) and the advent of IoT, is paving the way for the complete digitization and automation of industrial processes. Maintenance is one of these processes, where the introduction of a predictive approach, as opposed to the traditional techniques, is expected to considerably improve the industry maintenance strategies with gains such as reduced downtime, improved equipment effectiveness, lower maintenance costs, increased return on assets, risk mitigation, and, ultimately, profitable growth. With predictive maintenance, dedicated sensors monitor the critical points of assets. The sensor data then feed into machine learning algorithms that can infer the asset health status and inform operators and decision-makers. With this in mind, in this paper, we present TIP4.0, a platform for predictive maintenance based on a modular software solution for edge computing gateways. TIP4.0 is built around Yocto, which makes it readily available and compliant with Commercial Off-the-Shelf (COTS) or proprietary hardware. TIP4.0 was conceived with an industry mindset with communication interfaces that allow it to serve sensor networks in the shop floor and modular software architecture that allows it to be easily adjusted to new deployment scenarios. To showcase its potential, the TIP4.0 platform was validated over COTS hardware, and we considered a public data-set for the simulation of predictive maintenance scenarios. We used a Convolution Neural Network (CNN) architecture, which provided competitive performance over the state-of-the-art approaches, while being approximately four-times and two-times faster than the uncompressed model inference on the Central Processing Unit (CPU) and Graphical Processing Unit, respectively. These results highlight the capabilities of distributed large-scale edge computing over industrial scenarios.

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Section: Introductionmentioning

confidence: 79%

TIP4.0: Industrial Internet of Things Platform for Predictive Maintenance

Resende

Folgado

Oliveira

et al. 2021

Sensors

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“…The results presented in this work are intended to assess the feasibility and potential to apply the proposed method to predict the SoH of substation connectors instead of medium voltage connectors. For future work, this requires including an energy harvesting unit, several sensors (temperature, voltage drop and current) and wireless communications [4].…”

Section: Discussionmentioning

confidence: 99%

“…The electrical resistance has been effectively used as an indicator of the SoH of electrical connectors [4,5]. It is known that the natural ageing process of the connector increases the electrical resistance along its lifetime [6], thus increasing its operating temperature and tending to overheat the connector.…”

Section: Introductionmentioning

confidence: 99%

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State of Health Prediction of Power Connectors by Analyzing the Degradation Trajectory of the Electrical Resistance

2021

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Estimating the remaining useful life (RUL) or the state of health (SoH) of electrical components such as power connectors is still a challenging and complex task. Power connectors play a critical role in medium- and high-voltage power networks, their failure leading to important consequences such as power outages, unscheduled downtimes, safety hazards or important economic losses. Online condition monitoring strategies allow developing improved predictive maintenance plans. Due to the development of low-cost sensors and electronic communication systems compatible with Internet of Things (IoT) applications, several methods for online and offline SoH determination of diverse power devices are emerging. This paper presents, analyzes and compares the performance of three simple and effective methods for online determination of the SoH of power connectors with low computational requirements. The proposed approaches are based on monitoring the evolution of the connectors’ electrical resistance, which defines the degradation trajectory because the electrical resistance is a reliable indicator or signature of the SoH of the connectors. The methods analyzed in this paper are validated by means of experimental ageing tests emulating real degradation conditions. Laboratory results prove the suitability and feasibility of the proposed approach, which could be applied to other power products and apparatus.

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“…It is worth noting that the approach proposed in this paper, including the experimental results, are focused of confirming the viability of applying this method to determine the RUL of substation connectors. This implies adding an energy harvesting unit, different sensors (current, voltage drop and temperature) and wireless communications, thus turning the connector into a SmartConnector [ 33 ].…”

Section: Tested Connectors and Experimental Setup To Determine The Rul Of The Analyzed Connectorsmentioning

confidence: 99%

On-Line Remaining Useful Life Estimation of Power Connectors Focused on Predictive Maintenance

Riba

Gómez-Pau

Martinez

et al. 2021

Sensors

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Connections are critical elements in power systems, exhibiting higher failure probability. Power connectors are considered secondary simple devices in power systems despite their key role, since a failure in one such element can lead to major issues. Thus, it is of vital interest to develop predictive maintenance approaches to minimize these issues. This paper proposes an on-line method to determine the remaining useful life (RUL) of power connectors. It is based on a simple and accurate model of the degradation with time of the electrical resistance of the connector, which only has two parameters, whose values are identified from on-line acquired data (voltage drop across the connector, electric current and temperature). The accuracy of the model presented in this paper is compared with the widely applied autoregressive integrated moving average model (ARIMA), showing enhanced performance. Next, a criterion to determine the RUL is proposed, which is based on the inflection point of the expression describing the electrical resistance degradation. This strategy allows determination of when the connector must be replaced, thus easing predictive maintenance tasks. Experimental results from seven connectors show the potential and viability of the suggested method, which can be applied to many other devices.

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SmartConnector: A Self-Powered IoT Solution to Ease Predictive Maintenance in Substations

Cited by 32 publications

References 19 publications

TIP4.0: Industrial Internet of Things Platform for Predictive Maintenance

TIP4.0: Industrial Internet of Things Platform for Predictive Maintenance

State of Health Prediction of Power Connectors by Analyzing the Degradation Trajectory of the Electrical Resistance

On-Line Remaining Useful Life Estimation of Power Connectors Focused on Predictive Maintenance

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