Extended Kalman Filter Algorithm for Advanced Diagnosis of Positive Displacement Pumps

Dąbrowska, Anna; Stetter, Ralf; Sasmito, Hendra; Kleinmann, Stefan

doi:10.3182/20120829-3-mx-2028.00068

Cited by 6 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As the first, we can distinguish systems based on the model of the diagnosed object, e.g., by using an extended Kalman filter [ 2 ] as a state observer. Such a solution was used in certain papers [ 2 , 3 ] for the detection of failures of axial displacement pumps, which consisted of the evaluation of leaks developing in piston–cylinder pairs of these pumps. On the other hand, the paper by [ 4 ] used an adaptive Kalman filter algorithm to detect piston leakage in a hydraulic cylinder.…”

Section: Introductionmentioning

confidence: 99%

Use of the K-Nearest Neighbour Classifier in Wear Condition Classification of a Positive Displacement Pump

Konieczny

Stojek

2021

Sensors

View full text Add to dashboard Cite

This paper presents a learning system with a K-nearest neighbour classifier to classify the wear condition of a multi-piston positive displacement pump. The first part reviews current built diagnostic methods and describes typical failures of multi-piston positive displacement pumps and their causes. Next is a description of a diagnostic experiment conducted to acquire a matrix of vibration signals from selected locations in the pump body. The measured signals were subjected to time-frequency analysis. The signal features calculated in the time and frequency domain were grouped in a table according to the wear condition of the pump. The next step was to create classification models of a pump wear condition and assess their accuracy. The selected model, which best met the set criteria for accuracy assessment, was verified with new measurement data. The article ends with a summary.

show abstract

Section: Introductionmentioning

confidence: 99%

Use of the K-Nearest Neighbour Classifier in Wear Condition Classification of a Positive Displacement Pump

Konieczny

Stojek

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Physical models of hydraulic pumps were employed for fault detection and diagnosis by Yu [28], who used a bilinear fault detection observer to detect faults in the fluidic and mechanical domain. An extended Kalman filter is used in [29] for leakage detection in hydraulic pumps. A fault diagnosis method based on a nonlinear unknown input observer is proposed in [20] to realize intelligent hydraulic pump system fault detection.…”

Section: Introductionmentioning

confidence: 99%

A Model-Based Method for Leakage Detection of Piston Pump Under Variable Load Condition

2019

View full text Add to dashboard Cite

Hydraulic systems have been widely used in construction machinery, aeronautics, astronautics, automobiles, shipping, and other fields. Piston pumps are power source and core component in the hydraulic system. The detection and assessment of leakage fault in piston pumps is challenging because: 1) piston pump is always subjected to varying loads and operates at variable speeds, and the conventional condition monitoring methods that rely on instantaneous behavior analysis can hardly give reliable prediction to the actual health state of the piston pump and 2) it is not easy to obtain the typical condition monitoring data that can reflect the change of the piston pump in dynamic behavior when it suffers different severity levels of faults under various loading conditions. In order to tackle these issues, a model-based method for leakage detection of piston pump under variable load condition was proposed in this paper. The liquid-solid coupling model of piston pump is developed and verified at first. Then, with the aid of the model, different severity levels of oil leakage faults are simulated. It is found that outlet pressure signals can better indicate the oil leakage of piston pumps in comparison to the casing vibration signals. ''Sub-harmonics'' will appear in the frequency spectra of outlet pressure signals in the presence of oil leakage fault. Finally, the dynamic responses of the piston pump under different loading and structural health conditions are investigated systematically. The results show that external load can significantly influence the dynamic responses of the piston pump and the gradients of the trend lines of total ''sub-harmonic'' energy of outlet pressure provide correct prediction to the presence and growth of the oil leakage fault. Based on the investigation results, a reliable oil leakage detection and assessment method is proposed. INDEX TERMS Piston pump, leakage, liquid-solid coupling model, condition monitoring. WENXIAN YANG received the Ph.D. degree in mechanical engineering from Xi'an Jiaotong University, in 1999. Then, he did research in the

show abstract

Prediction of the Remaining Useful Life for Components of Automated Processes

Stetter

2019

Fault-Tolerant Design and Control of Automated Vehicles and Processes

View full text Add to dashboard Cite

Extended Kalman Filter Algorithm for Advanced Diagnosis of Positive Displacement Pumps

Cited by 6 publications

References 11 publications

Use of the K-Nearest Neighbour Classifier in Wear Condition Classification of a Positive Displacement Pump

Use of the K-Nearest Neighbour Classifier in Wear Condition Classification of a Positive Displacement Pump

A Model-Based Method for Leakage Detection of Piston Pump Under Variable Load Condition

Prediction of the Remaining Useful Life for Components of Automated Processes

Contact Info

Product

Resources

About