Anomaly Detection and Fault Prognosis for Bearings

Jin, Xiaohang; Sun, Yi; Que, Zijun; Wang, Yu; Chow, Tommy W. S.

doi:10.1109/tim.2016.2570398

Cited by 183 publications

(60 citation statements)

References 23 publications

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“…As a discrete system, the state model and observation model of the Stirling cryocooler are defined as equations (15) and (16). The two-exponential model that is generally used in the RUL prediction of bearings 21 and power batteries 31 is listed as following…”

Section: Particle Filtermentioning

confidence: 99%

“…In conclusion, model-based approaches may work well in RUL prediction of Stirling cryocoolers under small sample constraint. 20,21 Zio and Peloni 22 applied particle filtering (PF) to estimate the RUL of a mechanical component subject to fatigue crack growth. Miao et al 23 combined unscented Kalman filter and PF for RUL prediction, obtaining a prediction error less than 5%.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Health indicator construction and remaining useful life prediction for space Stirling cryocooler

Song

Liang

Teng

et al. 2019

Advances in Mechanical Engineering

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Stirling cryocoolers are widely used to refrigerate significant facilities in military and aerospace applications. However, under the influences of high-frequency piston motion and thermal environment deterioration, the refrigerating performance of Stirling cryocoolers will worsen inevitably, thus affecting the successful accomplishment of space mission. In this article, a methodology on assessing the performance of space Stirling cryocoolers is proposed, which involves the analysis of the failure mechanism, health indicator construction and remaining useful life prediction of the cryocooler. The potential factors affecting the refrigerating performance are discussed first. In view of these, three health indicators representing the degradation process of cryocoolers are constructed and then a multi-indicator method based on particle filter is proposed for remaining useful life prediction. Finally, the proposed method is validated by a Stirling cryocooler from one retired aircraft, and the results show that the constructed health indicators and remaining useful life prediciton approaches are effective for performance assessment of Stirling cryocooler.

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Section: Particle Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Health indicator construction and remaining useful life prediction for space Stirling cryocooler

Song

Liang

Teng

et al. 2019

Advances in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

“…To solve this problem, the autoregressive model was first used to filter out signals that are not fault related. (10) The filtered signals use the exponentially weighted moving average (EWMA) filter to remove the measurement noise and find an optimal estimate. The failure threshold for predicting the failure of the PCR instrument senses the onset of aging and estimates the RUL using an appropriate data-based method.…”

Section: Introductionmentioning

confidence: 99%

Fault Prognosis of Polymerase Chain Reaction Thermal Cycler Using Temperature Analysis

Kim¹,

Lee²,

Kim³

et al. 2019

Sensors and Materials

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In this paper, we propose an abnormal detection and defect prognostic method for a polymerase chain reaction (PCR) thermal cycler. PCR is a very important technology that is used to manipulate DNA or RNA in molecular biology. Thus, predicting the aging and residual useful life of the PCR device is essential to minimize the occurrence of catastrophic failure of the device. The proposed method predicts the aging and residual useful life of the PCR thermocycler by analyzing the change in the temperature regulation rate for each of the heating and cooling sections in the temperature profile. In this study, we analyzed three PCR thermal cyclers using the proposed method and showed that the proposed method can be used to measure aging.

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“…Equipment management is performed by corrective maintenance (CM) and by periodic preventive maintenance (PM) before the failure [1][2][3][4][5][6][7]. The development of the internet of things (IoT) technology allows even low-cost equipment to send sensor data to the cloud, changing the paradigm of equipment management [7].…”

Section: Introductionmentioning

confidence: 99%

Failure Prediction of Polymerase Chain Reaction Thermal Cycler

Lee¹,

Park²,

Kim³

et al. 2017

Advanced Science and Technology Letters

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Abstract. In this paper, we predict the aging of the PCR thermocycler by analyzing the heating and cooling rates of the PCR thermocycler. Two kinds of methods were applied to calculate the rise times of the heating and cooling protocol sections that were inversely related to the heating or cooling rates. The temporal changes of the rise times were investigated over several months. For three thermal cyclers with different structures, the increases of the rise times were fairly linear indicating the feasibility of the aging measurement.

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Anomaly Detection and Fault Prognosis for Bearings

Cited by 183 publications

References 23 publications

Health indicator construction and remaining useful life prediction for space Stirling cryocooler

Health indicator construction and remaining useful life prediction for space Stirling cryocooler

Fault Prognosis of Polymerase Chain Reaction Thermal Cycler Using Temperature Analysis

Failure Prediction of Polymerase Chain Reaction Thermal Cycler

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