Integrated approach for diagnostics and prognostics of HP LNG pump based on health state probability estimation

Kim, Hack-Eun; Hwang, Sung-Soo; Tan, Andy; Mathew, Joseph; Choi, Byeong-Keun

doi:10.1007/s12206-012-0850-4

Cited by 9 publications

(4 citation statements)

References 43 publications

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“…Vibration analysis, oil analysis, and infrared thermography monitor machinery health and detect flaws or degeneration. Prognostic approaches help diagnose defects by projecting their evolution and impact on equipment performance (Kim et al, 2012). Optimal Maintenance Scheduling: Diagnosis and prognostics match maintenance actions with expected machinery health and performance trends for optimal scheduling (Shin et al, 2018).…”

Section: Integration Of Diagnosis and Prognostics Strategiesmentioning

confidence: 99%

Investigation of Fault Diagnosis and Prognostics Techniques for Predictive Maintenance in Industrial Machinery

Varghese,

Ande,

Mahadasa

et al. 2023

Eng. int. (Dhaka)

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This research looks into fault detection and prognostic methods for industrial machinery predictive maintenance to maximize equipment dependability, reduce downtime, and improve operational effectiveness. The project aims to investigate integrated fault diagnosis and prognostics methodologies, analyze their applications in different industrial sectors, and determine policy implications to encourage implementation. Peer-reviewed articles, industry reports, case studies, and other current material are thoroughly reviewed as part of the technique. Major conclusions demonstrating the value of integrated fault diagnosis and prognostics in early fault identification, proactive decision-making, and optimal maintenance scheduling have been drawn from case studies in the power generating, petrochemical refining, and automotive manufacturing industries. The policy ramifications encompass the requirement for staff training, data standardization, investment in R&D, and regulatory frameworks to surmount constraints and stimulate innovation in industrial maintenance procedures. Organizations must adopt predictive maintenance technology to maintain competitiveness, cut expenses, and guarantee the dependable operation of vital mechanical assets in changing circumstances.

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Section: Integration Of Diagnosis and Prognostics Strategiesmentioning

confidence: 99%

Investigation of Fault Diagnosis and Prognostics Techniques for Predictive Maintenance in Industrial Machinery

Varghese,

Ande,

Mahadasa

et al. 2023

Eng. int. (Dhaka)

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“…Neural network has the ability to deal with complex multi-pattern matching and is an effective fault diagnosis method [3]. In 1996, Kim, H. E., Hwang, S et al used BP neural network to effectively identify inner ring defective bearings, outer ring defective bearings, roller defective bearings and some comprehensive fault characteristics, which can improve the efficiency of fault diagnosis [4]. In 2004, Shan, P. et al used the fast ICA algorithm to separate the vibration signal of the bearing, and then compared its power spectrum with the spectrum of the original vibration signal, the results show that ICA is easier to achieve early diagnosis of faults [5].…”

Section: Introductionmentioning

confidence: 99%

Network-based System of Mechanical Vibration Fault Diagnosis

Zhang,

Jin

2024

SCPE

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Nowadays researchers are investing in electrical machines fault diagnosis area. The users and manufacturers are strong for containing diagnostic features for reliability and scalability improvement. The regular monitoring enables machine faults early detection and hence helpful for automation by providing process control. The fault detection performance and machine-learning algorithms classification are highly dependent on features involved. The aim of this paper is to solve the network mechanical vibration problem and for that a research on mechanical vibration fault diagnosis is proposed. The first is to use the vibration signal receiving device to record the vibration signal of the target device. In the process of receiving the signal, the measurement point is related to the accuracy of the received signal, so it is necessary to prepare the measurement point. Secondly, the principle of fault detection based on vibration detection is introduced. The main purpose of this method is to identify the fault characteristics, simulate the fault with MATLAB, and obtain the error time-frequency diagram behavior. The feature vector dimension obtained by the idling confirmation example is the same as that of the rotor, which is 14, including 8 relative wavelet packet intensity entropy feature indexes and higher values, minimum value, peak-to-peak value, mean value, mean square error and variance. Finally, the deficiencies of the detected vibration faults are identified and similar improvements are proposed. Improvements only reduce signal vibration, disrupting feature isolation and identifying patterns.The observed percentage accuracy for classification of faults through proposed approach is 98.2%.

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“…For instance, John E.McInroy [1] proposed an algorithm for condition monitoring and fault diagnosis on motor instantaneous power and current spectrum of oil pump. Hack-Eun Kim [2] proposed a health state probability estimation algorithm to evaluate the remaining life of the device. D.Raheja [3] proposed a general state maintenance architecture to determine operating state of the equipment based on data fusion and data mining technology.…”

Section: Introductionmentioning

confidence: 99%

A Predict Method of Water Pump Operating State Based on Improved Particle Swarm Optimization of Support Vector Machine

Pan

2022

J. Phys.: Conf. Ser.

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In order to improve prediction accuracy of water pump operating state, a chaotic prediction model of the pump vibration data based on improved particle swarm optimization of support vector machine is proposed in this paper. Firstly, a grouping optimization strategy particle swarm algorithm based on cosine function is proposed. Then, the training set is obtained on the time series of vibration data by phase space reconstruction. Secondly, The improved particle algorithm is used to optimize the penalty parameters, insensitive loss coefficient and width parameters of support vector machine. Then, a prediction model of vibration data is established by using support vector machine combined with training set and optimal parameters. Finally, the operating state of the pump is predicted according to pump vibration measurement and evaluation method. Compared with the method of linear decreasing weight strategy, the method proposed in this paper is more accurately.

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Integrated approach for diagnostics and prognostics of HP LNG pump based on health state probability estimation

Cited by 9 publications

References 43 publications

Investigation of Fault Diagnosis and Prognostics Techniques for Predictive Maintenance in Industrial Machinery

Investigation of Fault Diagnosis and Prognostics Techniques for Predictive Maintenance in Industrial Machinery

Network-based System of Mechanical Vibration Fault Diagnosis

A Predict Method of Water Pump Operating State Based on Improved Particle Swarm Optimization of Support Vector Machine

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