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

Riba, Jordi-Roger; Gómez-Pau, Álvaro; Martinez, J.A.; Moreno-Eguilaz, Manuel

doi:10.3390/s21113739

Cited by 9 publications

(3 citation statements)

References 35 publications

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“…Noise cannot be accurately managed, however, RMSE can be improved for various mobile window lengths. Next, the author suggests using the Auto Regressive Integrated Moving Average (ARIMA) model to estimate the power connector system's remaining useful life [46]. When unexpected changes occur, performance may fall, but prediction accuracy is increased.…”

Section: Literature Reviewmentioning

confidence: 99%

Scheduling and Predictive Maintenance for Smart Toilet

2023

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Modern society needs bathrooms. Poor sanitation is caused by worn-out appliances and expensive cleaning. The technique also requires an inexpensive, dependable sensor. This study had three goals. Creating an IoT administration platform is the main goal. Literature evaluations assess the merits and downsides of existing systems. Second, we suggest predictive maintenance to assist predict bathroom equipment breakdowns. Finally, a scheduling algorithm was used to determine how many janitors to hire. We'll measure the model's effectiveness and make future recommendations. Infrared, temperature and humidity sensors create an IoT bathroom. Sensors have been studied to understand how to adapt them to the hygienic and private toilet environment. Sensor accuracy and cost-effectiveness could be enhanced with more development and testing. The Auto-Regressive Integrated Moving Average (ARIMA) model accurately predicts time series lags, making it a good candidate for predictive maintenance. Long Short-Term Memory (LSTM) is good in time series predictions, therefore it's fair to compare the two. We use the ARIMA model to handle Remaining Useful Life (RUL) prediction techniques by altering Moving Average (MA) and Auto-Regressive (AR). A genetic algorithm is used to create a janitorial cleaning schedule. The genetic algorithm was proposed to schedule cleaning workers. This approach improves the genetic algorithm by studying soft and hard scheduling restrictions. The Greedy algorithm is used to compare. Experimental evaluations reveal that the suggested model ARIGA meets both goals.

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Section: Literature Reviewmentioning

confidence: 99%

Scheduling and Predictive Maintenance for Smart Toilet

2023

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“…Current is injected with two excitation electrodes/contacts while two sensing instances are used to read current distribution modifications, forming a so-called four-point probe. Application examples include the inspection of electronic cabling [14] and connectors [15], monitoring cracks under fatigue loading [16][17][18][19], the estimation of geometrical dimensions as thickness [20] and weld nugget [21], and the mapping of local conductivity [22] and mechanical stress [23]. Provided an available access to the part conductive surface, CPD methods are a simple, reliable, and inexpensive NDT option.…”

Section: Introductionmentioning

confidence: 99%

Multisensor Inspection of Laser-Brazed Joints in the Automotive Industry

Machado

Rosado

Mendes

et al. 2021

Sensors

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Automobile laser brazing remains a complex process whose results are affected by several process variables that may result in nonacceptable welds. A multisensory customized inspection system is proposed, with two distinct non-destructive techniques: the potential drop method and eddy current testing. New probes were designed, simulated, produced, and experimentally validated in automobile’s laser-brazed weld beads with artificially introduced defects. The numerical simulations allowed the development of a new four-point probe configuration in a non-conventional orthogonal shape demonstrating a superior performance in both simulation and experimental validation. The dedicated inspection system allowed the detection of porosities, cracks, and lack of bonding defects, demonstrating the redundancy and complementarity these two techniques provide.

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“…Moreover, fault prognosis can be achieved by monitoring if estimated parameter values are above or below an accepted tolerance range [20]. Furthermore, an online state of health estimation of electrical devices is useful to estimate the RUL with high accuracy [21]. In this case, the RUL is continuously updated based on the actual state of the device.…”

Section: Introductionmentioning

confidence: 99%

CNN-LSTM-Based Prognostics of Bidirectional Converters for Electric Vehicles’ Machine

Rojas-Dueñas

Riba

Moreno-Eguilaz

2021

Sensors

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This paper proposes an approach to estimate the state of health of DC-DC converters that feed the electrical system of an electric vehicle. They have an important role in providing a smooth and rectified DC voltage to the electric machine. Thus, it is important to diagnose the actual status and predict the future performance of the converter and specifically of the electrolytic capacitors, in order to avoid malfunctioning and failures, since it is known they have the highest failure rates among power converter components. To this end, accelerated aging tests of the electrolytic capacitors are performed by applying an electrical overstress. The gathered data are used to train a CNN-LSTM model that is capable of predicting the future values of the capacitance and the equivalent series resistance (ESR) of the electrolytic capacitor. This model can be used to estimate the remaining useful life of the device, thus, increasing the reliability of the system and ensuring an adequate operating condition of the electric motor.

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

Cited by 9 publications

References 35 publications

Scheduling and Predictive Maintenance for Smart Toilet

Scheduling and Predictive Maintenance for Smart Toilet

Multisensor Inspection of Laser-Brazed Joints in the Automotive Industry

CNN-LSTM-Based Prognostics of Bidirectional Converters for Electric Vehicles’ Machine

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