Structural Fault Diagnosis of a Reciprocating Compressor Using Bond Graph Approach

Author(s):  
Ehsan Mollasalehi ◽  
Seyed Abdolali Zareian Jahromi ◽  
Mario Forcinito ◽  
Wei Hu ◽  
Qiao Sun

Condition monitoring and fault diagnosis of operating machinery have been of the main interests for many years. Existing systems employ data driven models which has limited prediction capability. On the contrary, a physics based model can predict phenomena that the system has not seen before. It also provides a means for root cause analysis. However, major obstacles include the complexity involved and real-time feasibility. In this paper, we use the bond graph technique to construct an analytical, physics-based model. We use a reciprocating compressor as an example since it is a good representation of multi-domain processes with a variety of possible failure modes. The bond graph model is cross examined with a finite element model and ultimately validated. To show how one can benefit from this approach, cracked piston rod is simulated to generate vibration signals that can potentially be picked up from accelerometers mounted on the exterior surface of the compressor.

10.29007/qj7v ◽  
2018 ◽  
Author(s):  
Carlos Alonso-González ◽  
Anibal Bregon ◽  
Belarmino Pulido ◽  
Matías Nacusse ◽  
Sergio Junco

Fault diagnosis is an essential part in the Health Management of autonomous vehicles. Within these vehicles the traction subsystem is a critical component, especially in those exploring planetary surfaces. Recent advances in brushless DC motors has raised the interest in new models and control configurations to integrate them in those vehicles due to their low energy consumption high torque/- mass ratio and low maintenance requirements. In this work we develop a full Bond Graph model of this subsystem, including the brushless motor and the control blocks needed for proper and efficient operation. These models will allow us to perform fault diagnosis with Bond Graph Possible Conflicts as the unifying formalism. We derive the Bond Graph-Possible Conflicts of the system, discussing the viability of the proposal.


Author(s):  
M. A. Djeziri ◽  
R. Merzouki ◽  
B. Ould Bouamama ◽  
G. Dauphin-Tanguy

2011 ◽  
Vol 128-129 ◽  
pp. 1438-1442
Author(s):  
Hua Yao Chang ◽  
Jun Zheng Wang ◽  
Jiang Bo Zhao ◽  
Shou Kun Wang

A fault diagnosis based on bond graph model is proposed for hydraulic variable pitch system. Because the knowledge representation of bond graph model can provide information of cause and effect between components, a bond graph model of hydraulic variable pitch system is given above rated wind speed. A fault tree, using cause and effect analysis by back propagation, is developed. Qualitative value of parameters is assigned and the fault source is detected by analyzing boundary parameters of fault tree. Comparing with quantitative fault diagnosis based on model, the bond graph fault diagnosis is more flexible and has good completeness.


2020 ◽  
Vol 92 (8) ◽  
pp. 1159-1168
Author(s):  
Jie Chen ◽  
Zhengdong Jing ◽  
Chentao Wu ◽  
Senyao Chen ◽  
Liye Cheng

Purpose This paper aims to improve the fault detection adaptive threshold of aircraft flap control system to make the system fault diagnosis more accurate. Design/methodology/approach According to the complex mechanical–electrical–hydraulic structure and the multiple fault modes of the aircraft flap control system, the advanced fault diagnosis method based on the bond graph (BG) model is presented, and based on the system diagnostic BG model, the parameter uncertainty intervals are estimated and a new adaptive threshold is constructed by linear fraction transformation. Findings To construct a more reasonable and accurate adaptive threshold range to more accurately detect system failures, some typical failure modes’ diagnosis process are selected and completed for verification; the simulation results show that the proposed method is effective and feasible for complex systems’ fault diagnosis. Practical implications This study can provide a theoretical guidance and technical support for fault diagnosis of complex systems, which avoid misdiagnosis and missed diagnosis. Originality/value This study enables more accurate fault detection and diagnosis of complex systems when considering factors such as parameter uncertainty.


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