Empirical investigation of acoustic emission signals for valve failure identification by using statistical method

Sim, Hoi Yin; Ramli, Rahizar; Saifizul, Ahmad; Abdullah, Munira

doi:10.1016/j.measurement.2014.08.028

Cited by 26 publications

(12 citation statements)

References 20 publications

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“…6b. The results were consistent with the observations from other work [2,15,18,43] where the energy level and RMS value of AE signal were applied as the machine condition indicators of other manufacturing systems. Figure 6c shows a line plot of C-ABS-Energy during this transition period.…”

Section: Experiments and Ae Data Analysissupporting

confidence: 91%

In situ monitoring of FDM machine condition via acoustic emission

Hai-xi

Wang

2015

Int J Adv Manuf Technol

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Fused deposition modeling (FDM) is one of the most popular additive manufacturing technologies for fabricating prototypes with complex geometry and different materials. However, current commercial FDM machines have the limitations in process reliability and product quality. In order to overcome these limitations and increase the levels of machine intelligence and automation, machine conditions need to be monitored more closely as in closedloop control systems. In this study, a new method for in situ monitoring of FDM machine conditions is proposed, where acoustic emission (AE) technique is applied. The proposed method allows for the identification of both normal and abnormal states of the machine conditions. The time-domain features of AE hits are used as the indicators. Support vector machines with the radial basis function kernel are applied for state identification. Experimental results show that this new method can potentially serve as a nonintrusive diagnostic and prognostic tool for FDM machine maintenance and process control.

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Section: Experiments and Ae Data Analysissupporting

confidence: 91%

In situ monitoring of FDM machine condition via acoustic emission

Hai-xi

Wang

2015

Int J Adv Manuf Technol

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“…Any abnormalities of the valve motion could be detected effectively by analyzing the RMS value. In their further study, the detection of other types of valve and the initiation of materials deformation in valves could be achieved [72]. Wang et al proposed a method for detecting the actual working condition of a valve using the AE signal and the simulated valve motion.…”

Section: The Failure Of Valvesmentioning

confidence: 99%

Nondestructive Detection of Valves Using Acoustic Emission Technique

Jin

Heng-hu

Hong

et al. 2015

Advances in Materials Science and Engineering

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The applications of acoustic emission (AE) technique in detection of valves are presented in this review, and the theoretical models and experimental results of nondestructive detection of valves using AE are provided. The generation of AE signals and the basic composition of AE detection system are briefly explained. The applications of AE technique in valves are focused on condition monitoring, failure, cavitation detection, and the development of portable measuring devices. All results prove that the AE technique works well in the detection of valves.

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“…Alfayez et al [18] investigated the AE application for cavitation detection in large scale centrifugal pump, they observed a clear relationship between AE RMS and the incipient cavitation. Sim et al [19] investigated the AE signal to identify the valve failure in reciprocating compressors. They use wavelet transform to decompose AE signal into different frequency ranges.…”

Section: Introductionmentioning

confidence: 99%

Automated Valve Fault Detection Based on Acoustic Emission Parameters and Artificial Neural Network

et al. 2019

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Reciprocating compressor is one of the most popular classes of machines use with wide applications in the industry. However, valve failures in this machine often results unplanned shutdown. Therefore, the effective valve fault detection technique is very necessary to ensure safe operation and to reduce the unplanned shutdown. This paper propose an artificial intelligence (AI) model to detect valve condition in reciprocating compressor based on acoustic emission (AE) parameters measurement and artificial neural network (ANN). A set of experiments were conducted on an industrial reciprocating air compressor with several operational conditions including good valve and faulty valve to acquire AE signal. A fault detection model was then developed from the combination of healthy-faulty data using ANN tool box available in MATLAB. The results of the model validation demonstrated accuracy of valves condition classification exceeding 97%. Eventually, the authors intend to do more efforts for programming this model in smart portable device which can be one of the innovative engineering technologies in the field of machinery condition monitoring in the near future.

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Empirical investigation of acoustic emission signals for valve failure identification by using statistical method

Cited by 26 publications

References 20 publications

In situ monitoring of FDM machine condition via acoustic emission

In situ monitoring of FDM machine condition via acoustic emission

Nondestructive Detection of Valves Using Acoustic Emission Technique

Automated Valve Fault Detection Based on Acoustic Emission Parameters and Artificial Neural Network

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