Entropy-based sensor selection for condition monitoring and prognostics of aircraft engine

Liu, Liansheng; Wang, Shaojun; Liu, Datong; Zhang, Yujie; Yu, Peng

doi:10.1016/j.microrel.2015.06.076

Cited by 62 publications

(28 citation statements)

References 15 publications

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“…In the past dozen years, sensor selection has been widely studied in various fields, e.g., estimation [6], target tracking [7], condition monitoring [8], to name a few. For parameter estimation in Kalman filtering dynamic system, [6] chose the optimal subset of sensors in each iteration via minimizing the error covariance matrix of the next iteration.…”

Section: Introductionmentioning

confidence: 99%

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A Gradient-Based Method for Robust Sensor Selection in Hypothesis Testing

Qian

Niu

et al. 2020

Sensors

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This paper considers the binary Gaussian distribution robust hypothesis testing under a Bayesian optimal criterion in the wireless sensor network (WSN). The distribution covariance matrix under each hypothesis is known, while the distribution mean vector under each hypothesis drifts in an ellipsoidal uncertainty set. Because of the limited bandwidth and energy, we aim at seeking a subset of p out of m sensors such that the best detection performance is achieved. In this setup, the minimax robust sensor selection problem is proposed to deal with the uncertainties of distribution means. Following a popular method, minimizing the maximum overall error probability with respect to the selection matrix can be approximated by maximizing the minimum Chernoff distance between the distributions of the selected measurements under null hypothesis and alternative hypothesis to be detected. Then, we utilize Danskin’s theorem to compute the gradient of the objective function of the converted maximization problem, and apply the orthogonal constraint-preserving gradient algorithm (OCPGA) to solve the relaxed maximization problem without 0/1 constraints. It is shown that the OCPGA can obtain a stationary point of the relaxed problem. Meanwhile, we provide the computational complexity of the OCPGA, which is much lower than that of the existing greedy algorithm. Finally, numerical simulations illustrate that, after the same projection and refinement phases, the OCPGA-based method can obtain better solutions than the greedy algorithm-based method but with up to 48.72 % shorter runtimes. Particularly, for small-scale problems, the OCPGA -based method is able to attain the globally optimal solution.

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Section: Introductionmentioning

confidence: 99%

“…The sensor selection problem for target tracking in large sensor networks was addressed in [7] based on generalized information gain. In [8], it provided an entropy-based sensor selection method for condition monitoring and prognostics of aircraft engine, which can describe the information contained in the sensor data sets.…”

Section: Introductionmentioning

confidence: 99%

A Gradient-Based Method for Robust Sensor Selection in Hypothesis Testing

Qian

Niu

et al. 2020

Sensors

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“…To build a compact PHM system, feature selection methods are helpful to select the most relevant input features in order to support the DL algorithm based PHM system. Liu et al proposed an entropy-based sensor selection approach for the aircraft engine [7]. A novel optimal feature selection approach was presented which can be a benefit for fault detection and diagnosis in smart buildings [8].…”

Section: Introductionmentioning

confidence: 99%

A Step-wise Feature Selection Scheme for a Prognostics and Health Management System in Autonomous Ferry Crossing Operation

Cheng

Ellefsen

et al. 2019

2019 IEEE International Conference on Mechatronics and Automation (ICMA)

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Developing a reliable algorithm to detect faults automatically within critical components in autonomous ferries is essential for safe and cost-beneficial maritime operations. Autonomous ferries are equipped with hundreds of sensors. Thus, in order to support the algorithm, the input data should be subjected to a feature selection process. This paper introduces a novel step-wise feature selection scheme for prognostics and health management (PHM) system in autonomous ferries. The scheme mainly consists of two steps. The first step is the Pearson correlation analysis to reduce the redundant information among sensors. In order to study the importance of the selected features obtained by correlation analysis and removal of irrelevant features, the second step is sensitivity analysis (SA) based feature selection. The proposed scheme is evaluated on realoperational marine diesel engine data. In the experiments, both fault classification and fault detection demonstrate the feasibility of the proposed approach.

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“…An entropy based sensor-selection approach has been proposed in Ref. [10] for an aerospace propulsion health monitoring system based on quantification of particular fault conditions and diagnostics. Sensor selection schemes were also proposed for tasks like target tracking and mission assignments in order to minimise the number of active sensors in a sensor network and hence reduce the energy use and prolong the lifetime of the sensor network [11].…”

Section: Introductionmentioning

confidence: 99%

Reducing sensor complexity for monitoring wind turbine performance using principal component analysis

Wang

Joyce

2016

Renewable Energy

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a b s t r a c tAvailability and reliability are among the priority concerns for deployment of distributed generation (DG) systems, particularly when operating in a harsh environment. Condition monitoring (CM) can meet the requirement but has been challenged by large amounts of data needing to be processed in real time due to the large number of sensors being deployed. This paper proposes an optimal sensor selection method based on principal component analysis (PCA) for condition monitoring of a DG system oriented to wind turbines. The research was motivated by the fact that salient patterns in multivariable datasets can be extracted by PCA in order to identify monitoring parameters that contribute the most to the system variation. The proposed method is able to correlate the particular principal component to the corresponding monitoring variable, and hence facilitate the right sensor selection for the first time for the condition monitoring of wind turbines. The algorithms are examined with simulation data from PSCAD/ EMTDC and SCADA data from an operational wind farm in the time, frequency, and instantaneous frequency domains. The results have shown that the proposed technique can reduce the number of monitoring variables whilst still maintaining sufficient information to detect the faults and hence assess the system's conditions.

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Entropy-based sensor selection for condition monitoring and prognostics of aircraft engine

Cited by 62 publications

References 15 publications

A Gradient-Based Method for Robust Sensor Selection in Hypothesis Testing

A Gradient-Based Method for Robust Sensor Selection in Hypothesis Testing

A Step-wise Feature Selection Scheme for a Prognostics and Health Management System in Autonomous Ferry Crossing Operation

Reducing sensor complexity for monitoring wind turbine performance using principal component analysis

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