An Integrated Deep Learning Method towards Fault Diagnosis of Hydraulic Axial Piston Pump

Tang, Shengnan; Yuan, Shouqi; Zhu, Yong; Li, Guangpeng

doi:10.3390/s20226576

Cited by 27 publications

(23 citation statements)

References 53 publications

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“…Wavelet transform is extensive employed in the domain of mechanical fault diagnosis. CWT of signal can be expressed as follows [ 43 , 44 , 45 ]:

where

is the wavelet basis function, which is obtained from the mother wavelet function through a series of expansion and translation.

is the scale factor, which is related to frequency, if the value is larger, the corresponding time resolution is poor and the frequency resolution is good.…”

Section: Continuous Wavelet Transformmentioning

confidence: 99%

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Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet

Zhu

Wang

et al. 2021

Sensors

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Hydraulic piston pump is the heart of hydraulic transmission system. On account of the limitations of traditional fault diagnosis in the dependence on expert experience knowledge and the extraction of fault features, it is of great meaning to explore the intelligent diagnosis methods of hydraulic piston pump. Motivated by deep learning theory, a novel intelligent fault diagnosis method for hydraulic piston pump is proposed via combining wavelet analysis with improved convolutional neural network (CNN). Compared with the classic AlexNet, the proposed method decreases the number of parameters and computational complexity by means of modifying the structure of network. The constructed model fully integrates the ability of wavelet analysis in feature extraction and the ability of CNN in deep learning. The proposed method is employed to extract the fault features from the measured vibration signals of the piston pump and realize the fault classification. The fault data are mainly from five different health states: central spring failure, sliding slipper wear, swash plate wear, loose slipper, and normal state, respectively. The results show that the proposed method can extract the characteristics of the vibration signals of the piston pump in multiple states, and effectively realize intelligent fault recognition. To further demonstrate the recognition property of the proposed model, different CNN models are used for comparisons, involving standard LeNet-5, improved 2D LeNet-5, and standard AlexNet. Compared with the models for contrastive analysis, the proposed method has the highest recognition accuracy, and the proposed model is more robust.

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“…Wavelet transform is extensive employed in the domain of mechanical fault diagnosis. CWT of signal can be expressed as follows [ 43 , 44 , 45 ]:

where

is the wavelet basis function, which is obtained from the mother wavelet function through a series of expansion and translation.

is the scale factor, which is related to frequency, if the value is larger, the corresponding time resolution is poor and the frequency resolution is good.…”

Section: Continuous Wavelet Transformmentioning

confidence: 99%

“…Wavelet transform is extensive employed in the domain of mechanical fault diagnosis. CWT of signal can be expressed as follows [43][44][45]:…”

Section: Continuous Wavelet Transformmentioning

confidence: 99%

Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet

Zhu

Wang

et al. 2021

Sensors

Self Cite

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show abstract

“…According to the literature [37], approximately 20% of all damages in hydraulic systems are pump damages, which are the key elements of the system. Due to the importance of this element, more effective methods of diagnosing them are in the interests of many researchers in the world [38][39][40]. One of the most popular types of hydraulic pumps is a variable displacement axial piston pump.…”

Section: Introductionmentioning

confidence: 99%

Wear Analysis of Additively Manufactured Slipper-Retainer in the Axial Piston Pump

Klimek¹,

Kluczyński²,

Łuszczek³

et al. 2022

Preprint

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The paper contains the results of the 100-hour test campaign of the Additive Manufactured (AM) spare part (retainer – slipper) dedicated for an exact axial piston pump. The material of the retainer-slipper has been identified by using energy dispersive spectroscopy and replaced by other – with similar material properties as the original one. The obtained spare part had been subjected to only one postprocessing type (sandblasting) to analyze the influence of the rough part after the AM process. The whole test campaign has been divided into stages, where after each stage microscopic measurements have been made. During microscopical investigation roughness and geometrical measurements were conducted. The results revealed that it is possible to replace parts in hydraulic pumps with the use of AM. 100-hour test campaign caused about 500% increase in the surface roughness of the pump’s original part which was cooperated with the AM spare retainer-slipper, without any damages to the test system.

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“…The article [ 12 ] proposes a damage detection system for a hydraulic axial piston pump. Given the changing operating conditions and the implication of frequent faults, it is difficult to accurately monitor machine faults in the actual operating process using up-to-date fault diagnosis methods.…”

Section: Introductionmentioning

confidence: 99%

Application of the MEMS Accelerometer as the Position Sensor in Linear Electrohydraulic Drive

Rybarczyk

2021

Sensors

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Various distance sensors are used as measuring elements for positioning linear electrohydraulic drives. The most common are magnetostrictive transducers or linear variable differential transformer (LVDT) sensors mounted inside the cylinder. The displacement of the actuator’s piston rod is proportional to the change in the value of the current or voltage at the output from the sensor. They are characterized by relatively low measurement noise. The disadvantage of presented sensors is the need to mount them inside the cylinders and the high price. The article presents preliminary research on the replacement of following sensors and the use of a microelectromechanical system (MEMS) accelerometer as a measuring element in the electrohydraulic drive control system. The control consisted of two phases: at first, the signal from the acceleration sensor was analyzed during the actuator movement, based on the value determined from the simplified model implemented on the controller. In the range of motion in which the dynamics were the lowest, the signal was integrated and the obtained value was used in the second phase of motion. In the correction phase, a new set point was determined. Conducting the research required building a dedicated research stand. The author conducted the simulation and experimental research.

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An Integrated Deep Learning Method towards Fault Diagnosis of Hydraulic Axial Piston Pump

Cited by 27 publications

References 53 publications

Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet

Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet

Wear Analysis of Additively Manufactured Slipper-Retainer in the Axial Piston Pump

Application of the MEMS Accelerometer as the Position Sensor in Linear Electrohydraulic Drive

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