Is Fault Detection and Diagnosis in Pneumatic Actuator A Topic of Concern?

Navada, Bhagya R.; Santhosh, K. V.

doi:10.37934/arfmts.77.2.102129

Cited by 2 publications

(1 citation statement)

References 109 publications

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“…The situation is more challenging for pneumatic systems due to the strong coupling of pressure, flowrate, and temperature of compressed air. Therefore, there are almost no established model-based fault diagnosis cases in terms of complex pneumatic systems except several cases on pneumatic components and small test benches [25][26][27][28][29][30][31].…”

Section: Literature Review Of Fault Detection and Diagnosis In Pneuma...mentioning

confidence: 99%

Facilitating Energy Monitoring and Fault Diagnosis of Pneumatic Cylinders with Exergy and Machine Learning

Wang,

Yang,

et al. 2023

TJFP

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Pneumatic systems are widely used in industrial production sectors. Increasing penetrations of Intelligent Manufacturing and Green Manufacturing are highlighting the drawbacks of pneumatic technology in terms of particularly low energy efficiency and low-level fault diagnosis intelligence. Here we propose that a combined energy-based maintenance and fault diagnostic approach for pneumatic systems could be a game-changer for pneumatics. In this study, a pneumatic cylinder with internal and external leakages is examined and a typical pneumatic experimental system is built. Exergy is adopted for evaluating the available energy of compressed air. Data-driven machine learning models, SAE + SoftMax neural network model and SAE + SVM model, are developed for fault detection and diagnosis. By comparing different machine learning methods with various pressure, flowrate, and exergy data, it is found that the diagnostic accuracy when using pressure and flowrate data is highly dependent on operating conditions, while the diagnostic accuracy when using exergy data is always high regardless of operating conditions. This indicates the promise of developing an exergy-based maintenance paradigm in pneumatic systems. Besides, with exergy and machine learning, more downstream faults can be detected and diagnosed with fewer upstream sensors. This study is the first attempt to develop an exergy-based maintenance paradigm in pneumatic systems. We hope it could inspire the following investigations in other energy domains.

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Section: Literature Review Of Fault Detection and Diagnosis In Pneuma...mentioning

confidence: 99%

Facilitating Energy Monitoring and Fault Diagnosis of Pneumatic Cylinders with Exergy and Machine Learning

Wang,

Yang,

et al. 2023

TJFP

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Low-cost Fault Diagnosis of Pneumatic Systems with Exergy and Machine Learning:

WANG,

ZHU,

XIONG

2023

JFPS International Journal of Fluid Power System

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Pneumatic systems are widely used in industrial manufacturing sectors. With the increasing penetration of intelligent and sustainable manufacturing, energy efficiency and fault diagnosis are being more and more significant. There is no exception even more urgent for pneumatic systems. In this study, a low-cost fault diagnosis concept for pneumatic systems is proposed by introducing exergy and machine learning. This concept is preliminarily verified in a typical simple pneumatic system with two parallel-installed cylinders. Stacked Auto-encoder (SAE) and one-dimensional convolutional neural network (1D CNN) are used for feature extraction of pressure, flow, and exergy data. Various machine learning algorithms, including Gaussian Process Classification (GPC), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Multi-Layer Perceptron (MLP), and Random Forest (RF) are used for learning and classifying different levels of external, internal, and compound leakage faults. Besides, the gradient-weighted class activation maps (Grad-CAM) are generated for interpreting the excellent performance of 1D CNN. The results show that it is feasible to accurately diagnose faults of multiple downstream cylinders with only one upstream sensor, thereby achieving low-cost fault diagnosis. Compared with pressure and flow data, the exergy data performs higher stability in accuracy and is less sensitive to different algorithms. Thus, we extrapolate that it is highly possible to implement fault diagnosis of complex pneumatic systems by deeply analyzing the energy data collected in energy management systems.

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Is Fault Detection and Diagnosis in Pneumatic Actuator A Topic of Concern?

Cited by 2 publications

References 109 publications

Facilitating Energy Monitoring and Fault Diagnosis of Pneumatic Cylinders with Exergy and Machine Learning

Facilitating Energy Monitoring and Fault Diagnosis of Pneumatic Cylinders with Exergy and Machine Learning

Low-cost Fault Diagnosis of Pneumatic Systems with Exergy and Machine Learning:

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