A methodology for detection of wear in hydraulic axial piston pumps

Lázaro, Jessica Gissella Maradey; Pinilla, Carlos Borrás

doi:10.1007/s12008-020-00681-w

Cited by 11 publications

(2 citation statements)

References 26 publications

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“…The classification results, produced by Naïve Bayes, support vector machine and neural networks for single operating points yield in classification rates between 80 % and almost 100 %. One of the latest methodologies is presented by (Maradey Lázaro & Borrás Pinilla, 2020), who present a comprehensive review for 14 publications regarding fault detection in axial piston pumps. They also develop a new methodology for volumetric efficiency decrease based on vibration signal acquisition, filtering, wavelet feature extraction, feature selection and artificial neural network training.…”

Section: Literature Reviewmentioning

confidence: 99%

Predictive Diagnosis in Axial Piston Pumps

Gnepper

Hitzer²,

Enge-Rosenblatt³

2023

IJPHM

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Increasing reliability, availability and safety requirements as well as an increasing amount of data acquisition systems have enabled condition-based maintenance in mobile and industrial machinery. In this paper, we present a methodology to develop a robust diagnostic approach. This includes the consideration of variable operating conditions in the data acquisition process as well as a versatile, non domain-specific feature extraction technique. By doing so, we train anomaly detection models for different fault types and different fault intensities in variable displacement axial piston pumps. Our specific interest points to the investigation of high-frequency condition indicators with a sampling rate of 1 MHz. Furthermore, we compare those to industry standard sensors, sampled with up to 20 kHz.By considering variable operating conditions, we are able to quantify the influence of the operating point. The results show, that high-frequency features are a suitable condition-indicator across several operating points and can be used to detect faults more easily. Although set up on a test-bench, the experimental design allows to draw conclusions about realistic field operational conditions.

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Section: Literature Reviewmentioning

confidence: 99%

Predictive Diagnosis in Axial Piston Pumps

Gnepper

Hitzer²,

Enge-Rosenblatt³

2023

IJPHM

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“…Numerous types of research have been carried out for the fault detection of an axial piston pump based on vibration signals. Methods based on cluster analysis [ 13 ], particle swarm optimization [ 14 ], artificial neural networks [ 15 ], hidden semi-Markov [ 16 ], and deep belief networks [ 17 , 18 ] are proposed, and features in the time domain (TD), frequency domain (FD), and time–frequency domains (TFD), including kurtosis, the root mean square, the energy ratio, the coefficients of the wavelet packet transform (WPT), and spectral entropy, are used as model indicators.…”

Section: Introductionmentioning

confidence: 99%

Piston Wear Detection and Feature Selection Based on Vibration Signals Using the Improved Spare Support Vector Machine for Axial Piston Pumps

Xia

Xiang

2022

Materials

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A piston wear fault is a major failure mode of axial piston pumps, which may decrease their volumetric efficiency and service life. Although fault detection based on machine learning theory can achieve high accuracy, the performance mainly depends on the detection model and feature selection. Feature selection in learning has recently emerged as a crucial issue. Therefore, piston wear detection and feature selection are essential and urgent. In this paper, we propose a vibration signal-based methodology using the improved spare support vector machine, which can integrate the feature selection into the piston wear detection learning process. Forty features are defined to capture the piston wear signature in the time domain, frequency domain, and time–frequency domain. The relevance and impact of sparsity in 40 features are illustrated through the single and multiple statistical feature analysis. Model performance is assessed and the sparse features are discovered. The maximum model testing and training accuracy are 97.50% and 96.60%, respectively. Spare features s10, s12, Ew(8), x7, Ee(5), and Ee(4) are selected and validated. Results show that the proposed methodology is applicable for piston wear detection and feature selection, with high model accuracy and good feature sparsity.

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