Remaining useful life prediction of aviation circular electrical connectors using vibration-induced physical model and particle filtering method

Sun, Bo; Yu, Li; Wang, Zili; Lu, Mang; Chen, Xueyong

doi:10.1016/j.microrel.2018.11.015

Cited by 37 publications

(15 citation statements)

References 15 publications

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“…Correct RUL prediction of power connectors is of great interest to ensure satisfactory reliability and safety of power systems, which provides reference data to develop maintenance plans and schedules, while allowing optimization of power system operational efficiency, to reduce costs due to unscheduled failures and to avoid premature connector faults and major power system failures [ 11 ]. Reliability analysis has been typically based on predicting failure rate [ 12 ]. Power connectors are designed for a continuous operation of several years, in the order of 30–40 years for substation connectors, although premature failure modes may happen significantly before [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

“…Electrical connectors’ reliability has been often characterized by the failure rate in spite of the misleading results and limitations of this approach [ 12 ]. Therefore, remaining useful life (RUL) prediction models are required, although there is a scarcity of literature for power connectors.…”

Section: Introductionmentioning

confidence: 99%

“…In [ 22 ], the mechanical behavior and fatigue lifetime of micro electrical connectors were estimated based on the effect of fretting wear. In [ 12 ], a RUL method for aviation connectors is presented based on high vibration stress, which combines a vibration-induced physical model, a particle filtering data-driven approach and accelerated degradation testing to evaluate the performance of the proposed approach. In [ 23 ], the reliability and failure rate of electrical connectors was predicted under particulate contamination and temperature stresses by means of accelerated tests to collect degradation data.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Riba

Gómez-Pau

Martinez

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…The combination of different operators will bring different effects in the denoising process. Some researchers have constructed some progressive methods of combination morphological filter such as (enhanced difference morphological filter (EDMF), adaptive smoothing multiscale morphological filtering (ASMMF), an adaptive rank-order morphological filter (ARMF), multi-scale morphology spectral (MMS), et al) in [3,4,5,6,7]. Based on this, an appropriate combination operator can be constructed to accelerate the rolling bearing.…”

Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis Method Based on Improved Adaptive Filtering and Joint Distribution Adaptation

2020

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In the real environment of industrial equipment, the vibration signals of essential components show deviations due to the fault and noise. Notably, the noise in the signal will interfere with the diagnosis process of the signal and reduce the accuracy of fault diagnosis. Based on the above problem, adaptive filtering (AF) is used as an excellent method to attenuate noise without specifying the noise type. However, how to define the most appropriate length and type of morphological filter element is the most inherent problem which needs to be solved first. This paper proposed a cooperative diagnosis method of rolling bearings vibration signal based on improved adaptive filtering and joint distribution adaptation (JDA). First, the kurtosis under different element types and lengths is calculated as an index. The structural element corresponding to maximum kurtosis is selected as the most suitable morphological filter element because the different morphological filter elements reflect the effect of feature extraction. Then, JDA aims to improve both the marginal distribution and the conditional distribution to solve the chaotic distribution of time-domain features under variable working conditions. Finally, the improved least squares support vector machine (LSSVM) verified the effectiveness and improvement of the proposed method under bearing acceleration signal. At the same time, the comparative experiment proved that the proposed method not only directly corrects the most appropriate elements greatly optimizes the feature structure, but also enhances the accuracy of fault diagnosis. INDEX TERMS adaptive filters, morphological operation, time domain features, transfer learning, joint distribution adaptation, LSSVM, fault diagnosis.

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“…Several failure prediction methods were applied on various type of electrical contacts. A failure prediction method of circular electrical connector by oxide-film accumulation mechanism and particle filter method was introduced in [15]. Residual life of electrical connector predicted by intermitted failure and hidden Semi-Markov model was introduced in [16].…”

Section: Introductionmentioning

confidence: 99%

Deterioration Behavior Analysis and LSTM-Based Failure Prediction of GIB Electrical Contact Inside Various Insulation Gases

et al. 2020

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Plug-in connector of gas insulated bus (GIB) could be subject to the wear and electrical contact resistance (ECR) deterioration processes under cyclic mechanical and current loads. A specific test platform is designed to simulate the current-carrying wear and ECR degradation process of Ag-plated GIB electrical contact spot inside SF6, N2 and Air insulation mediums under current loads. Worn surface morphology and chemical composition are observed and analyzed. Influence of insulation medium and current load on deterioration mechanism of GIB electrical contact are discussed. Raw ECR curve obtained from current-carrying experiment is smoothed by the exponentially weighted moving average (EWMA) method. A long short-term memory (LSTM) neural network is then proposed to realize both single-step and multi-step ECR and failure predictions of GIB electrical contact. Results show that adhesive wear on contact surface happened due to materials transfer. Deterioration of contact spot inside air medium is much severer than those in SF6 and N2 due to oxidation effect. Deterioration of contact spot inside SF6 medium under high current load is severer than those under low current load due to the change of wear behavior and chemical reactions. Compared with other methods, LSTM could achieve better performance in both singlestep and multi-step failure prediction of GIB electrical contact. INDEX TERMS electrical contact resistance (ECR); gas insulated bus (GIB); insulation gases; LSTM.

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Remaining useful life prediction of aviation circular electrical connectors using vibration-induced physical model and particle filtering method

Cited by 37 publications

References 15 publications

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

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

A Fault Diagnosis Method Based on Improved Adaptive Filtering and Joint Distribution Adaptation

Deterioration Behavior Analysis and LSTM-Based Failure Prediction of GIB Electrical Contact Inside Various Insulation Gases

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