A Markov chains prognostics framework for complex degradation processes

Chiachío, Juan; Jalón, María L.; Chiachío, Manuel; Kolios, Athanasios

doi:10.1016/j.ress.2019.106621

Cited by 43 publications

(16 citation statements)

References 24 publications

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“…This remarkable correlation suggest that the DoH index can be used to predict the crack length in a probabilistic manner. Furthermore, a state-space dynamical system representation of the data will provide a predictive model of the crack length whereby the remaining useful life of the structure can be estimated (see [ 56 ]), using just the DoH as input data. This further development would require the assessment of a higher number of specimens, which would provide additional data to build a robust predictive model.…”

Section: Discussionmentioning

confidence: 99%

Structural Health Monitoring Using Ultrasonic Guided-Waves and the Degree of Health Index

Cantero‐Chinchilla

Aranguren

Royo

et al. 2021

Sensors

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This paper proposes a new damage index named degree of health (DoH) to efficiently tackle structural damage monitoring in real-time. As a key contribution, the proposed index relies on a pattern matching methodology that measures the time-of-flight mismatch of sequential ultrasonic guided-wave measurements using fuzzy logic fundamentals. The ultrasonic signals are generated using the transmission beamforming technique with a phased-array of piezoelectric transducers. The acquisition is carried out by two phased-arrays to compare the influence of pulse-echo and pitch-catch modes in the damage assessment. The proposed monitoring approach is illustrated in a fatigue test of an aluminum sheet with an initial notch. As an additional novelty, the proposed pattern matching methodology uses the data stemming from the transmission beamforming technique for structural health monitoring. The results demonstrate the efficiency and robustness of the proposed framework in providing a qualitative and quantitative assessment for fatigue crack damage.

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Section: Discussionmentioning

confidence: 99%

Structural Health Monitoring Using Ultrasonic Guided-Waves and the Degree of Health Index

Cantero‐Chinchilla

Aranguren

Royo

et al. 2021

Sensors

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“…In the last decades, a great variety of damage prognosis techniques have been developed in SHM depending on the availability of physics knowledge and data. From the perspective of how the prognosis models are formulated, they can be distinguished into physics-based [1][2][3], data-driven [4][5][6][7][8] and hybrid methods [9][10][11][12][13]. Physics-based methods utilize specific mechanistic knowledge and theories to formulate a pure physics-based model, which describes the structural degradation phenomena as well as the links between the damage states and the SHM measurements.…”

Section: Introductionmentioning

confidence: 99%

“…Physics-based methods utilize specific mechanistic knowledge and theories to formulate a pure physics-based model, which describes the structural degradation phenomena as well as the links between the damage states and the SHM measurements. On the other hand, data-driven methods, resorting to datadriven modelling techniques such as neural networks [6] and Markov chains [8], attempt to use amounts of data to build the relationship between the internal degradation behaviour and the external observations. Hybrid methods, taken as a combination of the two above methods, usually consider the physics-based process and data-driven measurement equations to describe the damage evolution and the mapping between the damage state and measurement, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…However, given the uncertainties arising from the complex structural degradation behaviour, the environment effects and the sensor health conditions [16,17], both the deterministic physic-based and data-driven models are unable to provide an accurate prognostic result. A well-acknowledged strategy for both the physics-based [1][2][3] and datadriven methods [6,8] to improve their prognostic performance is to set the parameters within the corresponding models as unknown components to be updated by a state estimation technique, such as particle filter (PF) given its good performance in nonlinear and non-Gaussian problem. On the other hand, this strategy is not fully exploited in hybrid methods, where the parameters in physics-based process equation are usually taken as unknown variables to be estimated by PF, while the parameters relating to the data-driven measurement equation are not [11,18,19].…”

Section: Introductionmentioning

confidence: 99%

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Particle Filter-Based Hybrid Damage Prognosis Considering Bias

Li¹,

Cadini²,

Sbarufatti³

2021

Proceedings of the 8th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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Hybrid prognosis combining both the physical knowledge and data-driven techniques has shown great potential for damage prognosis in structural health monitoring (SHM). Current practices consider the physics-based process and data-driven measurement equations to describe the damage evolution and the mapping between the damage state and the SHM signal (or the feature extracted from SHM signal), respectively. However, the bias between the measurements predicted by data-driven equation and the actual SHM measurements, arising from uncertainties like damage geometries and sensor placement or noise, can lead to inaccurate prognosis results. To account for this problem, this paper adopts a methodology typically applied for sensor fault diagnosis, and develops a new hybrid state space model with a bias parameter included into the state vector and the measurement equation. Particle filter (PF) serves as the estimation technique to identify the state and parameters relating to the damage as well as the bias parameter, and RUL can be predicted by the PF estimates and physics-based process equation. The numerical study about the fatigue crack growth shows the new model together with PF can provide satisfactory estimation and prediction results in case of bias in the measurement model.

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“…However, such methods need to assume prior probability and a life distribution such as a Gaussian or Weibull distribution with a linear relationship. However, for RUL prediction, the relationship between measurements is nonlinear, and the assumed probabilistic distribution contradicts the actual situation (Tang et al, 2019;Chiachío et al, 2020). Also, estimating the transfer probability matrix often require a large amount of training data (Papadopoulos et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction Based on Improved Temporal Convolutional Network for Nuclear Power Plant Valves

Wang

Peng

et al. 2020

Front. Energy Res.

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Proper risk assessment and monitoring of critical component is crucial to the safe operation of Nuclear Power Plants. One of the ways to ensure real-time monitoring is the development of Prognostics and Health Management systems for safety-critical equipment. Recently, the remaining useful life prediction (RUL) has been found to be important in ensuring predictive maintenance and avoiding critical component failure. With the development of artificial intelligent techniques, deep learning algorithms are becoming popular for RUL prediction. Consequently, this paper presents RUL prediction techniques for nuclear plant electric gate valves with a temporal convolution network (TCN). The main advantage of using TCN is its ability to capture and process useful information in short-term sensor measurement changes. Moreover, the efficiency of the proposed TCN is enhanced by incorporating a convolution auto-encoder as a preprocessing layer in its structure, which greatly improved the residual convolution mode. The proposed method is verified on the electric gate valves experimental dataset that represents the real-world operation of the valve, and the result obtained is compared with other conventional data-driven approaches. The evaluation result shows impressive performance of the proposed model in predicting the remaining service life of the gate valves used in the nuclear reactor control system. Moreover, the generalization of the proposed model is evaluated on the turbofan engine benchmark dataset. The evaluation result also shows improved performance in the predicted RUL. Broader application of the proposed TCN is envisaged for critical components in other industries.

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A Markov chains prognostics framework for complex degradation processes

Cited by 43 publications

References 24 publications

Structural Health Monitoring Using Ultrasonic Guided-Waves and the Degree of Health Index

Structural Health Monitoring Using Ultrasonic Guided-Waves and the Degree of Health Index

Particle Filter-Based Hybrid Damage Prognosis Considering Bias

Remaining Useful Life Prediction Based on Improved Temporal Convolutional Network for Nuclear Power Plant Valves

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