Detection and isolation of parametric faults in hydraulic pumps using a set-based approach and quantitative–qualitative fault specifications

Hast, Daniel; Findeisen, Rolf; Streif, Stefan

doi:10.1016/j.conengprac.2015.01.003

Cited by 20 publications

(6 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As APUs are a major component in drive systems, several authors have dealt with fault detection and diagnosis in hydraulic pumps, most of them using a methodology based on vibration signals for the assessment. Physic based approaches are present (Hast, Findeisen, & Streif, 2015), (Bayer & Enge-Rosenblatt, 2011b) and (Bayer & Enge-Rosenblatt, 2011a), but data-driven approaches are far more common due to the high complexity of the systems and non-linear effects due to viscous friction and contacts (Lan et al, 2018). During our research, we investigated 39 papers and articles in the research area of fault detection and diagnosis in hydraulic pumps, from which 72 % make use of vibration signals, 33 % use pressure signals and 20 % state that volumetric flow rate is a quantity with informative content.…”

Section: Literature Reviewmentioning

confidence: 99%

Predictive Diagnosis in Axial Piston Pumps

Gnepper

Hitzer²,

Enge-Rosenblatt³

2023

IJPHM

View full text Add to dashboard Cite

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.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Predictive Diagnosis in Axial Piston Pumps

Gnepper

Hitzer²,

Enge-Rosenblatt³

2023

IJPHM

View full text Add to dashboard Cite

show abstract

“…Z. Ma et al [5] led research on hydraulic pump faults by using a model-based diagnosis system, while T. Li et al [6] used multiscale information for predicting the remaining useful time of a piston pump. D. Hast et al [7] also worked on fault specification for the detection of a piston pump fault. C. Lu et al [8] developed a fault diagnosis method for hydraulic pumps used in aeronautic application based on the evidence theory.…”

Section: Introductionmentioning

confidence: 99%

A Matrix FMEA Analysis of Variable Delivery Vane Pumps

2021

View full text Add to dashboard Cite

Hydraulic systems are widely used in the aeronautic, machinery, and energy industries. The functions that these systems perform require high reliability, which can be achieved by examining the causes of possible defects and failures and by taking appropriate preventative measures. One of the most popular methods used to achieve this goal is FMEA (Failure Modes and Effects Analysis), the foundations of which were developed and implemented in the early 1950s. It was systematized in the following years and practically implemented. It has also been standardized and implemented as one of the methods of the International Organization for Standardization (ISO) 9000 series standards on quality assurance and management. Apart from wide application, FMEA has a number of weaknesses, which undoubtedly include risk analysis based on the RPN (Risk Priority Number), which is evaluated as a product of severity, occurrence, and detection. In recent years, the risk analysis has been very often replaced by fuzzy logic. This study proposes the use of matrix analysis and statistical methods for performing simplified RCA (Root Cause Analysis) and for classification potential failures for a variable delivery vane pump. The presented methodology is an extension of matrix FMEA and allows for prioritizing potential failures and their causes in relation to functions performed by pump components, the end effects, and the defined symptoms of failure of the vane pump.

show abstract

“…For the piston pump, due to the complex structure, nonlinear relationship between internal variables and the liquid-solid coupling effect, the accurate mathematical model is very difficult to obtain. As a result, data-driven diagnosis methods are commonly used [8,9]. Considering easy measurement and low cost, the pressure signal and vibration signal were usually observed for fault diagnosis of piston pump.…”

Section: Introductionmentioning

confidence: 99%

A multi-fault diagnosis method for piston pump in construction machinery based on information fusion and PSO-SVM

Tang¹,

Wang²,

Wu³

2019

J. vibroeng.

View full text Add to dashboard Cite

Piston pumps are key components in construction machinery, the failure of which may cause long delay of the construction work and even lead to serious accident. Because construction machines are exposed to poor working conditions, multiple faults of piston pumps are most likely to occur simultaneously. When multiple faults occur together, it is difficult to detect. A multi-fault diagnosis method for piston pump based on information fusion and PSO-SVM is proposed in this thesis. Information fusion is used as fault feature extraction and PSO-SVM is applied as the fault mode classifier. According to the method, vibration signal and pressure signal of piston pump in normal state, single fault state and multi-fault state are collected at first. Then the empirical mode decomposition (EMD) is used to decompose vibration signals into different frequency band and energy features are extracted. These energy features extracted from vibration signals and time-domain features extracted from pressure signal are information fused at the feature layer and constitute the eigenvectors. Finally, these eigenvectors are put into support vector machine (SVM) and the working conditions of piston pump were classified. Particle swarm optimization (PSO) is applied to optimize two parameters of SVM. The experimental results show that the recognition accuracy of the normal state, three single failure modes and multi-fault modes are 98.3 %, 97.6 % and 94 % respectively. These recognition accuracies are higher than which using vibration signal or pressure signal alone. So, the proposed method can not only identify the single fault, but also effectively identify the multi-fault of piston pump.

show abstract

Detection and isolation of parametric faults in hydraulic pumps using a set-based approach and quantitative–qualitative fault specifications

Cited by 20 publications

References 22 publications

Predictive Diagnosis in Axial Piston Pumps

Predictive Diagnosis in Axial Piston Pumps

A Matrix FMEA Analysis of Variable Delivery Vane Pumps

A multi-fault diagnosis method for piston pump in construction machinery based on information fusion and PSO-SVM

Contact Info

Product

Resources

About