Degradation detection for internal gear pumps using pressure reduction time maps

In this paper, a novel approach for detecting degradation in internal gear pumps is proposed. In a data-driven approach, pressure reduction time maps (PRTMs) are identified as a useful indicator for degradation detection. A PRTM measures the time for reducing the internal pump pressure from certain levels to any lower level when the pump engine is stopped and the valves are closed. The PRTM can thus be interpreted as an internal leakage indicator of the pump. For simplified evaluation, PRTMs are compressed to a single scalar indicator by computing their volume (PRTMV). When the internal leakage increases due to wear, the pressure in the pump decreases faster (implying a decreased PRTMV). The proposed approach has been developed and tested with data of real internal gear pumps with different operating times. The PRTMV shows a close relation to the operating time of the pump. Moreover, we compare PRTMV with the commonly used and well known approach of observing pressure holding speed (PHS). Especially for medium degradation, PRTMV shows better sensitivity then PHS.

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Detecting wear in internal gear pumps by observing the pressure reduction time

2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) ◽

10.1109/aim43001.2020.9158866 ◽

2020 ◽

Author(s):

Kurt Pichler ◽

Rainer Haas ◽

Veronika Putz ◽

Christian Kastl

Keyword(s):

Pressure Reduction ◽

Reduction Time ◽

Gear Pumps ◽

Internal Gear

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Novel Approach for Calibrating Freeway Highway Multi-Regimes Fundamental Diagram

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198120930221 ◽

2020 ◽

Vol 2674 (9) ◽

pp. 561-574

Author(s):

Emmanuel Kidando ◽

Alican Karaer ◽

Boniphace Kutela ◽

Angela E. Kitali ◽

Ren Moses ◽

...

Keyword(s):

Traffic Flow ◽

Bayesian Model ◽

Potential Benefit ◽

Data Driven ◽

Fundamental Diagram ◽

Novel Approach ◽

Proposed Model ◽

Density Relationship ◽

Data Driven Approach ◽

Flow Variables

For almost a century, several models have been developed to calibrate the pairwise relationship between traffic flow variables, that is, speed, density, and flow. Multi-regime models are well known for being superior over single-regime models in fitting the speed–density relationship. However, in modeling multi-regime models, breakpoints that separate the regimes are visually established based on the subjective judgment of data characteristics. Thus, this study proposes a data-driven approach to estimate the breakpoints of multi-regime models. It applies the Bayesian model for calibrating multi-regime models (two and three-regime models) for fitting traffic flow fundamental diagram. Furthermore, the analysis presented accounts for the random characteristics associated with the flow. To demonstrate the application of the proposed algorithm, traffic flow data from Interstate 10 (I-10) freeway in Jacksonville, Florida, were used in the analysis. The results demonstrate the potential benefit of using the proposed model in calibrating the fundamental diagram. The proposed approach can also quantify uncertainty and encode prior knowledge about the breakpoints in the model if the model developer wishes.

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