Fault-diagnosis for reciprocating compressors using big data and machine learning

Qi, Guanqiu; Zhu, Zhiqin; Erqinhu, Ke; Chen, Yinong; Chai, Yi; Sun, Jia Lin

doi:10.1016/j.simpat.2017.10.005

Cited by 99 publications

(61 citation statements)

References 17 publications

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“…Considering that the largest number of failures is associated with valves of compressor cylinders of the first stage, an experimental assessment of their efficiency and tightness is necessary, which is one of the mandatory steps in the process of checking the safety of a compressor unit [8,9].…”

Section: Engineeringmentioning

confidence: 99%

Methodology of Experimental Investigations of Valve Operation

Seyidahmadov¹

2019

EUREKA: Physics and Engineering

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Tightness, as well as the reliability of the valve plate, is a complex property of the effective operation of compressor cylinders of the first stage and, in general, gas-engine reciprocating compressors. The issue of valve plate tightness is a subject of independent study, since technical and economic efficiency depends on their work. In this connection, only some data obtained under operating conditions are presented in this work. As a research result, it is found that, taking into account the identified requirements for the gas lift system, in order to effectively increase the operating hours of valves with increased tightness of the plate, it is necessary to check and purge the valves. Therefore, each valve in the gas lift compressor station, without subjecting them to cleaning, is first recommended to check for leaks. To confirm the feasibility of checking valve tightness, special equipment is offered for each gas-lift compressor station, a purge chamber, on which the tightness of valve plates is checked. The usefulness and importance of the purge chamber is in preparation of the valve at the gas lift compressor station, which contributes to increased efficiency, safe operation, normal tightness and reliability of its operation.

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Section: Engineeringmentioning

confidence: 99%

Methodology of Experimental Investigations of Valve Operation

Seyidahmadov¹

2019

EUREKA: Physics and Engineering

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“…Presently, widely-used mechanical fault prediction methods employ artificial neural networks (ANNs) [4][5][6], support vector machines (SVMs) [7,8], deep learning [9][10][11], and other artificial intelligence (AI) technologies. For example, Ben et al [12] proposed the use of empirical mode decomposition and energy entropy for feature extraction, which was combined with an ANN for multifeature fusion to make bearing fault predictions.…”

Section: Introductionmentioning

confidence: 99%

Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data

Huang

Liu

2019

Sensors

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Vibration sensing data is an important resource for mechanical fault prediction, which is widely used in the industrial sector. Artificial neural networks (ANNs) are important tools for classifying vibration sensing data. However, their basic structures and hyperparameters must be manually adjusted, which results in the prediction accuracy easily falling into the local optimum. For data with high levels of uncertainty, it is difficult for an ANN to obtain correct prediction results. Therefore, we propose a multifeature fusion model based on Dempster-Shafer evidence theory combined with a particle swarm optimization algorithm and artificial neural network (PSO-ANN). The model first used the particle swarm optimization algorithm to optimize the structure and hyperparameters of the ANN, thereby improving its prediction accuracy. Then, the prediction error data of the multifeature fusion using a PSO-ANN is repredicted using multiple PSO-ANNs with different single feature training to obtain new prediction results. Finally, the Dempster-Shafer evidence theory was applied to the decision-level fusion of the new prediction results preprocessed with prediction accuracy and belief entropy, thus improving the model's ability to process uncertain data. The experimental results indicated that compared to the K-nearest neighbor method, support vector machine, and long short-term memory neural networks, the proposed model can effectively improve the accuracy of fault prediction.

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“…Furthermore, from a maintenance point of view, the use of advanced techniques can support the real time performance control during production, implementing faults detection [12][13][14][15], fault diagnosis [16,17] and remaining useful life estimation to support the optimization of manufacturing management and maintenance scheduling [18].…”

Section: Introductionmentioning

confidence: 99%

Real Time Energy Performance Control for Industrial Compressed Air Systems: Methodology and Applications

et al. 2019

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Most manufacturing and process industries require compressed air to such an extent that in Europe, for instance, about 10% of the total electrical energy consumption of industries is due to compressed air systems (CAS). However, energy efficiency in compressed air production and handling is often ignored or underestimated, mainly because of the lack of awareness about its energy consumption, caused by the absence of proper measurements on CAS in most industrial plants. Therefore, any effective energy saving intervention on generation, distribution and transformation of compressed air requires proper energy information management. In this paper we demonstrate the importance of monitoring and controlling energy performance in compressed air generation and use, to enable energy saving practices, to enhance the outcomes of energy management projects, and to obtain additional benefits for non-energy-related activities, such as operations, maintenance management and energy accounting. In particular, we propose a novel methodology based on measured data, and baseline definition through statistical modelling and control charts. The proposed methodology is tested on a real compressed air system of a pharmaceutical manufacturing plant in order to verify its effectiveness and applicability.

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Fault-diagnosis for reciprocating compressors using big data and machine learning

Cited by 99 publications

References 17 publications

Methodology of Experimental Investigations of Valve Operation

Methodology of Experimental Investigations of Valve Operation

Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data

Real Time Energy Performance Control for Industrial Compressed Air Systems: Methodology and Applications

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