Assessment of characteristics of acoustic emission parameters for valve damage detection under varying compressor speeds

Abstract

Acoustic emission technique is often employed to detect valve abnormalities. With the development of technology, machine learning-based fault diagnosis methods are prevalent in the nondestructive testing industry as they can automatically detect valve problems without any human intervention. Nevertheless, feeding in all possible input parameters into the learning algorithm without any prior assessment may result in high computational cost and time, while adding to the risk of having false alarms. This study intended to obtain characteristics of acoustic emission signal for various valve conditions and compressor speeds by examining the four most commonly used parameters, namely the acoustic emission root mean square, acoustic emission crest factor, acoustic emission variance, and acoustic emission kurtosis. The study begins with time–frequency analysis of one revolution acoustic emission signal acquired from a faulty suction valve through discrete wavelet transform to obtain the signal characteristics of valve events. To associate signals with valve movements, the reconstructed discrete wavelet transform signals are further segregated into six time segments, and the four acoustic emission parameters are computed from each of the time segments. These parameters are analyzed through statistical analysis namely the two-way analysis of variance, followed by the Tukey test to obtain the best parameter which can differentiate each valve condition clearly at all speeds. The results revealed that acoustic emission root mean square is the best parameter especially in identification of heavy grease valve condition during suction valve opening event while acoustic emission crest factor is capable to detect leaky valve during the suction valve closing event at all speeds. It is believed that effective valve diagnosis strategy can be delivered by referring to the features of parameters and the characteristic valve event timing corresponding to each valve condition and speed.

Structural damage detection of adaptive tangent-based unsaturated bistable stochastic resonance

“…Moreover, different tangent points x t have different effects on Kramers rate in TUBSR system under the same parameter a, b. Based on equation (8), the output SNR t of TUBSR can be obtained, which is shown in equation (9).…”

“…4 In the early damage stage or strong noise environment, the damage characteristics are severely contaminated by noise, which makes some damage difficult to detect. However, the damage characteristics of weak signal can be extracted by appropriate signal processing method to achieve the damage detection of mechanical structures, [5][6][7][8] such as wavelet transform, singular value decomposition, etc. The principle of these methods is to suppress the noise in the signal, but some damage characteristics can be eliminated during the denoising process.…”

Applications of Machine Learning to Reciprocating Compressor Fault Diagnosis: A Review

“…With the SVM model, the leakage of the pipeline valve could be recognized. Sim et al [67] employed the time-frequency analysis of the AE signal through the discrete wavelet transform (DWT) and assessed the characteristics of four acoustic emission parameters [67]. The result revealed that the acoustic emission root mean square (RMS) performed the best.…”

A new method for reciprocating compressor fault diagnosis based on indicator diagram feature extraction

“…Han et al 12 proposed a quantitative diagnosis method for valve leakage of reciprocating compressors based on the system feature diagnosis method, and the application results show that this method can realize the quantitative diagnosis of valve leakage faults. Sim et al 13 obtained the characteristics of acoustic emission signals of compressors under different valve conditions, and conducted statistical analysis of acoustic emission root mean square, acoustic emission factor, acoustic emission variance and acoustic emission kurtosis. The results show that the acoustic emission root mean square is the best parameter to identify the valve fault, and the fault diagnosis of the valve is successfully realized by using this parameter.…”