A hybrid data-driven modeling method on sensor condition monitoring and fault diagnosis for power plants

Chen, Jianhong; Li, Hongkun; De-ren, Sheng; Li, Wei

doi:10.1016/j.ijepes.2015.03.012

Cited by 33 publications

(14 citation statements)

References 28 publications

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“…These factors have a great impact on the feasibility of installation, when it is difficult to use wired solutions, either by harsh location or high number of sensor nodes used and also because of the easy maintenance and cabling reduced costs. As main advantages, Chen et al (Chen et al, 2015) pinpoints the large coverage area, ubiquitous information, fast communication via RF and self-organisation throughout the direct communication between entities. A Smart Sensor Platform (Ramamurthy et al, 2007) was developed, which applies the plug and play concept by means of hardware interface, payload, communication between sensors and actuators, and ultimately allows for software update using 'over-the-air' programming.…”

Section: Industrial Wireless Sensor Networkmentioning

confidence: 99%

“…A Smart Sensor Platform (Ramamurthy et al, 2007) was developed, which applies the plug and play concept by means of hardware interface, payload, communication between sensors and actuators, and ultimately allows for software update using 'over-the-air' programming. Cao et al (Cao et al, 2008) developed a distributed approach to put closer sensors and actuators in a collaborative environment using WSNs and, Chen et al (Chen et al, 2015), push this approach forward considering the same methodology, but taking into account all the industrial domain restrictions. Despite the huge potential of WSNs, these solutions still have several issues and future research challenges.…”

Section: Industrial Wireless Sensor Networkmentioning

confidence: 99%

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Wireless Sensor Network Simulation for Fault Detection in Industrial Processes

Pinto

Rossetti

Gonçalves

2016

Proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

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Abstract:Sensor data is extremely important to monitor machines at the shop-floor level and its environmental surrounding conditions for condition-based monitoring, machine diagnosis and process adaptation to new requirements. Based on the described scope, self-diagnostics and self-organizing capabilities are core functionalities of any Industrial Wireless Sensor Network (IWSN). In the present work, a simulated case study was developed with the main intent of validating techniques implemented for sensor data diagnosis of error detection and equipment failure. The scenarios explored try to mimic some common situations of a manufacturing environment when dealing with WSNs, where a piece of sensor equipment suddenly stops working or an unpredictable change in the environment leads to faulty data readings. This paper introduces Castalia and describes how it was used to simulate a direct application of an Optical Metrology System on an industrial Resistance Spot Welding process, which is composed of a camera and several luminosity sensors. More specifically, a sensor data validation module was proposed, implemented and used to extend Castalia functionalities.

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Section: Industrial Wireless Sensor Networkmentioning

confidence: 99%

Section: Industrial Wireless Sensor Networkmentioning

confidence: 99%

Wireless Sensor Network Simulation for Fault Detection in Industrial Processes

Pinto

Rossetti

Gonçalves

2016

Proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

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“…The DFIM Volt per Hertz drive system consists of a DFIM powered through the grid in stator side and a PWM inverter in the rotor side [1]- [4].…”

Section: Classic Dfim Modelingmentioning

confidence: 99%

A DFIM Sensor Faults Multi-Model Diagnosis Approach Based on an Adaptive PI Multiobserver—Experimental Validation

Abid¹,

Benhamed²,

Sbita³

2015

IJMNTA

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This paper studies the problem of diagnosis strategy for a doubly fed induction motor (DFIM) sensor faults. This strategy is based on unknown input proportional integral (PI) multiobserver. Thecontribution of this paper is on one hand the creation of a new DFIM model based on multi-model approach and, on the other hand, the synthesis of an adaptive PI multi-observer. The DFIM Volt per Hertz drive system behaves as a nonlinear complex system. It consists of a DFIM powered through a controlled PWM Voltage Source Inverter (VSI). The need of a sensorless drive requires soft sensors such as estimators or observers. In particular, an adaptive Proportional-Integral multi-observer is synthesized in order to estimate the DFIM's outputs which are affected by different faults and to generate the different residual signals symptoms of sensor fault occurrence. The convergence of the estimation error is guaranteed by using the Lyapunov's based theory. The proposed diagnosis approach is experimentally validated on a 1 kW Induction motor. Obtained simulation results confirm that the adaptive PI multiobserver consent to accomplish the detection, isolation and fault identification tasks with high dynamic performances.

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“…The major problem with hardware redundancy is the cost (including the sensor cost and maintenance cost). In this context, approaches based on analytical redundancy are proposed in the literature, including artificial neural networks (ANN) [7][8][9], independent component analysis (ICA) [10,11], support vector machine (SVM) [12,13], fuzzy logic [14][15][16], partial least-squares regression (PLSR) [17], and PCA [18][19][20][21][22][23][24]. A study conducted by Hines and Seibert concluded that the simplicity of analytical 2 Science and Technology of Nuclear Installations redundancy techniques and the tractability of their uncertainty calculations could favor them for acceptance by regulatory bodies [25].…”

Section: Introductionmentioning

confidence: 99%

Condition Monitoring of Sensors in a NPP Using Optimized PCA

Peng

Liu

et al. 2018

Science and Technology of Nuclear Installations

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An optimized principal component analysis (PCA) framework is proposed to implement condition monitoring for sensors in a nuclear power plant (NPP) in this paper. Compared with the common PCA method in previous research, the PCA method in this paper is optimized at different modeling procedures, including data preprocessing stage, modeling parameter selection stage, and fault detection and isolation stage. Then, the model's performance is greatly improved through these optimizations. Finally, sensor measurements from a real NPP are used to train the optimized PCA model in order to guarantee the credibility and reliability of the simulation results. Meanwhile, artificial faults are sequentially imposed to sensor measurements to estimate the fault detection and isolation ability of the proposed PCA model. Simulation results show that the optimized PCA model is capable of detecting and isolating the sensors regardless of whether they exhibit major or small failures. Meanwhile, the quantitative evaluation results also indicate that better performance can be obtained in the optimized PCA method compared with the common PCA method.

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A hybrid data-driven modeling method on sensor condition monitoring and fault diagnosis for power plants

Cited by 33 publications

References 28 publications

Wireless Sensor Network Simulation for Fault Detection in Industrial Processes

Wireless Sensor Network Simulation for Fault Detection in Industrial Processes

A DFIM Sensor Faults Multi-Model Diagnosis Approach Based on an Adaptive PI Multiobserver—Experimental Validation

Condition Monitoring of Sensors in a NPP Using Optimized PCA

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