Remaining Useful Life Estimation with Lamb-Wave Sensors Based on Wiener Process and Principal Components Regression

Mishra, Spandan; Vanli, O. Arda

doi:10.1007/s10921-015-0328-2

Cited by 20 publications

(8 citation statements)

References 25 publications

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“…Balka et al reviewed some methods of cure rate models based on the first passage times, using Wiener processes, in [23]. Mishra and Vanli, in [24], used principal component regression and Wiener process degradation modeling for predicting the RUL of a structure from the Lamb wave sensor data. The principal component regression was used for extracting damagesensitive features, and the Wiener process was developed to model the random growth.…”

Section: Related Workmentioning

confidence: 99%

Remaining Useful Life Prediction for Rotating Machinery Based on Optimal Degradation Indicator

Qin¹,

Zhang²,

Hu³

et al. 2017

Shock and Vibration

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Remaining useful life (RUL) prediction can provide early warnings of failure and has become a key component in the prognostics and health management of systems. Among the existing methods for RUL prediction, the Wiener-process-based method has attracted great attention owing to its favorable properties and flexibility in degradation modeling. However, shortcomings exist in methods of this type; for example, the degradation indicator and the first predicting time (FPT) are selected subjectively, which reduces the prediction accuracy. Toward this end, this paper proposes a new approach for predicting the RUL of rotating machinery based on an optimal degradation indictor. First, a genetic programming algorithm is proposed to construct an optimal degradation indicator using the concept of FPT. Then, a Wiener model based on the obtained optimal degradation indicator is proposed, in which the sensitivities of the dimensionless parameters are utilized to determine the FPT. Finally, the expectation of the predicted RUL is calculated based on the proposed model, and the estimated mean degradation path is explicitly derived. To demonstrate the validity of this model, several experiments on RUL prediction are conducted on rotating machinery. The experimental results indicate that the method can effectively improve the accuracy of RUL prediction.

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Section: Related Workmentioning

confidence: 99%

Remaining Useful Life Prediction for Rotating Machinery Based on Optimal Degradation Indicator

Qin¹,

Zhang²,

Hu³

et al. 2017

Shock and Vibration

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“…e statistical-based life prediction method models the data with a stochastic model to achieve RUL prediction. Mishra and Vanli [20] proposed a prediction model based on the principal component regression and Wiener process degradation model to predict the remaining life of a sensor. In order to avoid the need for subjective selection of degradation indicator and first prediction time in the Wiener process, Aisong et al [21] proposed a Wiener process model for earthquake prediction based on optimal degradation indicator.…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction of Rolling Bearings Using Electrostatic Monitoring Based on Two-Stage Information Fusion Stochastic Filtering

Zhang

Wang

2020

Mathematical Problems in Engineering

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The accurate prediction of the remaining useful life (RUL) of rolling bearings is of great significance for a rational formulation of maintenance strategies and the reduction of maintenance costs. According to the two-stage nonlinear degradation characteristics of rolling bearing operation, this paper proposes a prognosis model based on modified stochastic filtering. First, multiple features reextracted from the time domain, frequency domain, and complexity angles, and the baseline Gaussian mixture model (GMM) is established using the normal operating data after spectral regression. The Bayesian-inferred distance (BID) is used as a quantitative indicator to reflect the bearing performance degradation degree. Then, taking multiparameter fusion results as input, the relationship between BID and remaining life is established by the two-stage stochastic filtering model to realize online dynamic remaining useful life prediction. The method in this paper overcomes the difficulty of accurately defining the failure threshold of rolling bearing. At the same time, it reduces the computational burden, avoiding the need of calculating the joint probability distribution for high-dimensional data. Finally, the proposed method has been verified experimentally to have high precision and engineering application value.

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“…Mishra and Vanli [41] have used principal component regression (PCR) to predict delamination on a composite structure. The predicted delaminations were then used in a Weiner process-based degradation model to predict the remaining useful life of the structure.…”

Section: Introductionmentioning

confidence: 99%

Detection of Gaps in Concrete–Metal Composite Structures Based on the Feature Extraction Method Using Piezoelectric Transducers

Giri

Mishra

Clark

et al. 2019

Sensors

Self Cite

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A feature extraction methodology based on lamb waves is developed for the non-invasive detection and prediction of the gap in concrete–metal composite structures, such as concrete-filled steel tubes. A popular feature extraction method, partial least squares regression, is utilised to predict the gaps. The data is collected using the piezoelectric transducers attached to the external surface of the metal of the composite structure. A piezoelectric actuator generates a sine burst signal, which propagates along the metal and is received by a piezoelectric sensor. The partial least squares regression is performed on the raw sensor signal to extract features and to determine the relationship between the signal and the gap size, which is then used to predict the gaps. The applicability of the developed system is tested on two concrete-metal composite specimens. The first specimen consisted of an aluminium plate and the second specimen consisted of a steel plate. This technique is able to detect and predict gaps as low as 0.1 mm. The results demonstrate the applicability of this technique for the gap and debonding detection in concrete-filled steel tubes, which are critical in determining the degree of composite action between concrete and metal.

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Remaining Useful Life Estimation with Lamb-Wave Sensors Based on Wiener Process and Principal Components Regression

Cited by 20 publications

References 25 publications

Remaining Useful Life Prediction for Rotating Machinery Based on Optimal Degradation Indicator

Remaining Useful Life Prediction for Rotating Machinery Based on Optimal Degradation Indicator

Remaining Useful Life Prediction of Rolling Bearings Using Electrostatic Monitoring Based on Two-Stage Information Fusion Stochastic Filtering

Detection of Gaps in Concrete–Metal Composite Structures Based on the Feature Extraction Method Using Piezoelectric Transducers

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