A New Approach for Vibration-based Rolling Bearings Fault Detection in Non-Stationary Operating Conditions

2017 ◽  
pp. 347-362
Author(s):  
R. Golafshan ◽  
M. Wegerhoff ◽  
G. Jacobs ◽  
K. Y. Sanliturk
Keyword(s):  
Fault Detection ◽  
Operating Conditions ◽  
Rolling Bearings ◽  
New Approach

scholarly journals A Hierarchical Decision Fusion Diagnosis Method for Rolling Bearings

2021 ◽  
Vol 11 (2) ◽  
pp. 739
Author(s):  
Jingzhou Fei ◽  
Xinran Lv ◽  
Yunpeng Cao ◽  
Shuying Li
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Signal Reconstruction ◽  
Decision Fusion ◽  
Fault Isolation ◽  
Operating Conditions ◽  
Majority Voting ◽  
Rolling Bearings ◽  
Hierarchical Decision ◽  
Diagnosis Method

In order to achieve accurate fault diagnosis of rolling bearings, a hierarchical decision fusion diagnosis method for rolling bearings is proposed. The hierarchical back propagation neural networks (BPNNs) architecture includes a fault detection layer, fault isolation layer and fault degree identification layer, which reduce the calculation cost and enhance the maintainability of the fault diagnosis algorithm. By wavelet packet decomposition and signal reconstruction of the raw vibration signal of a rolling bearing, the time-domain features of the reconstructed signals are extracted as the input of each BPNN and the accuracy of fault detection, fault isolation and degree estimation are improved. By using the majority voting method, the diagnosis results of multiple BPNNs are fused, which avoids the missed diagnosis and misdiagnosis caused by the insensitivity of a vibration characteristic to a specific fault. Finally, the proposed method is verified experimentally. The results show that the proposed method can accurately detect the fault of rolling bearings, recognize the fault location and estimate the fault severity under different operating conditions.

scholarly journals A Comparative Study on Fault Detection Methods for Gas Turbine Combustion Systems

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 389
Author(s):  
Jinfu Liu ◽  
Zhenhua Long ◽  
Mingliang Bai ◽  
Linhai Zhu ◽  
Daren Yu
Keyword(s):  
Fault Detection ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Operating Conditions ◽  
Detection Methods ◽  
Distribution Model ◽  
Outlet Temperature ◽  
Combustion System ◽  
Comparison Results ◽  
Gas Turbine Combustion

As one of the core components of gas turbines, the combustion system operates in a high-temperature and high-pressure adverse environment, which makes it extremely prone to faults and catastrophic accidents. Therefore, it is necessary to monitor the combustion system to detect in a timely way whether its performance has deteriorated, to improve the safety and economy of gas turbine operation. However, the combustor outlet temperature is so high that conventional sensors cannot work in such a harsh environment for a long time. In practical application, temperature thermocouples distributed at the turbine outlet are used to monitor the exhaust gas temperature (EGT) to indirectly monitor the performance of the combustion system, but, the EGT is not only affected by faults but also influenced by many interference factors, such as ambient conditions, operating conditions, rotation and mixing of uneven hot gas, performance degradation of compressor, etc., which will reduce the sensitivity and reliability of fault detection. For this reason, many scholars have devoted themselves to the research of combustion system fault detection and proposed many excellent methods. However, few studies have compared these methods. This paper will introduce the main methods of combustion system fault detection and select current mainstream methods for analysis. And a circumferential temperature distribution model of gas turbine is established to simulate the EGT profile when a fault is coupled with interference factors, then use the simulation data to compare the detection results of selected methods. Besides, the comparison results are verified by the actual operation data of a gas turbine. Finally, through comparative research and mechanism analysis, the study points out a more suitable method for gas turbine combustion system fault detection and proposes possible development directions.

scholarly journals Fault Detection in DC Microgrids Using Short-Time Fourier Transform

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 277
Author(s):  
Ivan Grcić ◽  
Hrvoje Pandžić ◽  
Damir Novosel
Keyword(s):  
Fourier Transform ◽  
Fault Detection ◽  
Operating Conditions ◽  
Time Signal ◽  
Short Time Fourier Transform ◽  
Dc Microgrid ◽  
Protection Scheme ◽  
Simulink Model ◽  
Battery Energy ◽  
Short Time

Fault detection in microgrids presents a strong technical challenge due to the dynamic operating conditions. Changing the power generation and load impacts the current magnitude and direction, which has an adverse effect on the microgrid protection scheme. To address this problem, this paper addresses a field-transform-based fault detection method immune to the microgrid conditions. The faults are simulated via a Matlab/Simulink model of the grid-connected photovoltaics-based DC microgrid with battery energy storage. Short-time Fourier transform is applied to the fault time signal to obtain a frequency spectrum. Selected spectrum features are then provided to a number of intelligent classifiers. The classifiers’ scores were evaluated using the F1-score metric. Most classifiers proved to be reliable as their performance score was above 90%.

scholarly journals Fault Detection and Severity Level Identification of Spiral Bevel Gears under Different Operating Conditions Using Artificial Intelligence Techniques

Machines ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 173
Author(s):  
Syed Muhammad Tayyab ◽  
Steven Chatterton ◽  
Paolo Pennacchi
Keyword(s):  
Artificial Intelligence ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Research Study ◽  
High Load ◽  
Operating Conditions ◽  
Spiral Bevel Gears ◽  
Bevel Gears ◽  
Spiral Bevel ◽  
Level Identification

Spiral bevel gears are known for their smooth operation and high load carrying capability; therefore, they are an important part of many transmission systems that are designed for high speed and high load applications. Due to high contact ratio and complex vibration signal, their fault detection is really challenging even in the case of serious defects. Therefore, spiral bevel gears have rarely been used as benchmarking for gears’ fault diagnosis. In this research study, Artificial Intelligence (AI) techniques have been used for fault detection and fault severity level identification of spiral bevel gears under different operating conditions. Although AI techniques have gained much success in this field, it is mostly assumed that the operating conditions under which the trained AI model is deployed for fault diagnosis are same compared to those under which the AI model was trained. If they differ, the performance of AI model may degrade significantly. In order to overcome this limitation, in this research study, an effort has been made to find few robust features that show minimal change due to changing operating conditions; however, they are fault discriminating. Artificial neural network (ANN) and K-nearest neighbors (KNN) are used as classifiers and both models are trained and tested by using the selected robust features for fault detection and severity assessment of spiral bevel gears under different operating conditions. A performance comparison between both classifiers is also carried out.

Development of a 2-D CFD Approach for Computing 3-D Honeycomb Labyrinth Leakage

2003 ◽  
Author(s):  
Dong-Chun Choi ◽  
David L. Rhode
Keyword(s):  
Labyrinth Seal ◽  
Algebraic Model ◽  
Operating Conditions ◽  
Cfd Model ◽  
Resource Requirement ◽  
New Approach ◽  
Computer Resource ◽  
Flow Through ◽  
D Model ◽  
Model Approach

A new approach for employing a 2-D CFD model to approximately compute a 3-D flow field such as that in a honeycomb labyrinth seal was developed. The advantage of this approach is that it greatly reduces the computer resource requirement needed to obtain a solution of the leakage for the 3-D flow through a honeycomb labyrinth. After the leakage through the stepped labyrinth seal was measured, it was used in numerically determining the value of one dimension (DTF1) of the simplified geometry 2-D approximate CFD model. Then the capability of the 2-D model approach was demonstrated by using it to compute the 3-D flow that had been measured at different operating conditions, and in some cases different distance to contact values. It was found that very close agreement with measurements was obtained in all cases, except for that of intermediate clearance and distance to contact for two sets of upstream and downstream pressure. The 2-D approach developed here offers interesting benefits relative to conventional algebraic-equation models, particularly for evaluating labyrinth geometries/operating conditions that are different from that of the data employed in developing the algebraic model.

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