Enhanced fault diagnosis of roller bearing elements using a combination of empirical mode decomposition and minimum entropy deconvolution

2016 ◽  
Vol 231 (4) ◽  
pp. 655-671 ◽  
Author(s):  
Z Zhang ◽  
M Entezami ◽  
E Stewart ◽  
C Roberts
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Early Stage ◽  
Roller Bearing ◽  
Fault Detection And Diagnosis ◽  
New Technique ◽  
Minimum Entropy ◽  
Processing Chain ◽  
Mode Decomposition ◽  
Detection And Diagnosis

This paper introduces a new signal processing algorithm for vibration-based fault detection and diagnosis of roller bearings. The methodology proposed in this paper is based on the combination of two data-adaptive techniques which are further enhanced through the use of an automatic feature identification mechanism. The new technique, introduced as empirical mode envelope with minimum entropy, combines elements from the empirical mode decomposition (EMD) and minimum entropy deconvolution (MED) approaches with an energy moment technique to improve the feature selection stage of the EMD algorithm. This improvement allows the processing chain to identify early stage roller bearing faults in noisier signals. The energy moment technique is used to automatically identify the most appropriate intrinsic mode function from the EMD process prior to the MED algorithm being applied. This is in contrast to conventional approaches which tend to use the first mode or make selections based on traditional energy techniques. The combination of the adaptive techniques of EMD and MED allows the development of an improved technique for fault detection and diagnosis of signals. Combining these techniques with the energy moment approach allows further improved fault detection in complex non-stationary conditions. The processing chain has been tested using data obtained during laboratory testing. From the experimental results, it is shown that the new technique is capable of the detection of early stage (minor) roller and outer race defects found in tapered-roller-bearings rotating at a variety of speeds and noise scenarios.

EMD and Envelope Spectrum Based Bearing Fault Detection

2012 ◽  
Vol 459 ◽  
pp. 233-237 ◽  
Author(s):  
Zhen Tao Li ◽  
Hui Li
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Fault Detection And Diagnosis ◽  
Mode Decomposition ◽  
Analysis Technique ◽  
Oscillation Modes ◽  
Intrinsic Oscillation ◽  
Detection And Diagnosis ◽  
Envelope Spectrum

A novel method to fault diagnosis of bearing based on empirical mode decomposition (EMD) and envelope spectrum is presented. EMD method is self-adaptive to non-stationary and non-linear signal. The methodology developed in this paper decomposes the original vibration signal in intrinsic oscillation modes, using the empirical mode decomposition. Then the envelope spectrum is applied to the selected intrinsic mode function that stands for the bearing faults. The basic principle is firstly introduced in detail. Then the EMD is applied in the research of the fault detection and diagnosis of the bearing. The experimental results show that the proposed method based on EMD and envelope spectrum analysis technique can effectively diagnose the faults of bearing.

A Method of Fault Detection and Diagnosis Based on Time-Frequency Analysis

2012 ◽  
Vol 490-495 ◽  
pp. 1407-1410
Author(s):  
Ying Bo Liang ◽  
Li Hong Zhang ◽  
Jin Li
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Maximum Modulus ◽  
Fault Detection And Diagnosis ◽  
Time Frequency ◽  
Multi Scale ◽  
Mode Function ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Detection And Diagnosis

In the paper the authors propose a combination of the EMD (empirical mode decomposition)method and the wavelet analysis to suppress the noise and fault detection and diagnosis, It adopts empirical mode decomposition to current signal ,obtained a series of IMFs(Intrinsic Mode Function),removing the first IMF component to denosing,and then analyzed multi-scale ,using signal become mutated have the maximum modulus determine the time that the failure appeared ,the results show that this method determine the time that the failure appeared.

scholarly journals Fault Detection and Diagnosis of Air Handling Unit: A Review

2019 ◽  
Vol 255 ◽  
pp. 06001 ◽  
Author(s):  
Cheng Yew Leong
Keyword(s):  
Fault Detection ◽  
Air Conditioning ◽  
Early Stage ◽  
Fault Detection And Diagnosis ◽  
Commercial Building ◽  
Air Handling Unit ◽  
Operations And Maintenance ◽  
Detection And Diagnosis ◽  
Air Conditioning Systems ◽  
And Control

Air-conditioning systems consumed the most energy usage nearly 45% of the total energy used in commercial-building. Where AHU is one of the most extensively operated equipment and this device is typical customize and complex which can results in hardwire failure and controller errors. The efficiency of the system is very much depending on the proper functioning of sensors. Faults arising from the sensors and control systems are a major contribution to the energy wastage. As such faults often go unnoticed for extended periods of time until the deterioration in performance becomes great enough to trigger comfort complaints or total equipment failure. Energy could be reduced if those faults can be detected and identified at early stage. This paper aims to review of various existing automated fault detection and diagnosis (AFDD) methods for an Air Handling Unit. The background of AHU system, general fault detection and diagnosis framework and typical faults in AHU is described. Comparison and evaluation of the various methodologies will be reviewed in this paper. This comparative study also reveals the strengths and weaknesses of the different approaches. The important role of fault diagnosis in the broader context of air- conditioning is also outlined. By identifying and diagnosing faults to be repaired, these techniques can benefits building owners by reducing energy consumption, improving indoor air quality and operations and maintenance.

The Combination of Empirical Mode Decomposition and Minimum Entropy Deconvolution for Roller Bearing Diagnostics in Non-Stationary Operation

2012 ◽  
Author(s):  
R. Ricci ◽  
P. Borghesani ◽  
S. Chatterton ◽  
P. Pennacchi
Keyword(s):  
Signal Processing ◽  
Empirical Mode Decomposition ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Minimum Entropy ◽  
Bearing Diagnostics ◽  
Mode Decomposition ◽  
Data Adaptive ◽  
The Hilbert Transform ◽  
Processing Techniques

Diagnostics is based on the characterization of mechanical system condition and allows early detection of a possible fault. Signal processing is an approach widely used in diagnostics, since it allows directly characterizing the state of the system. Several types of advanced signal processing techniques have been proposed in the last decades and added to more conventional ones. Seldom, these techniques are able to consider non-stationary operations. Diagnostics of roller bearings is not an exception of this framework. In this paper, a new vibration signal processing tool, able to perform roller bearing diagnostics in whatever working condition and noise level, is developed on the basis of two data-adaptive techniques as Empirical Mode Decomposition (EMD), Minimum Entropy Deconvolution (MED), coupled by means of the mathematics related to the Hilbert transform. The effectiveness of the new signal processing tool is proven by means of experimental data measured in a test-rig that employs high power industrial size components.

scholarly journals Hybrid Random Forest and Support Vector Machine Modeling for HVAC Fault Detection and Diagnosis

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8163
Author(s):  
Wunna Tun ◽  
Johnny Kwok-Wai Wong ◽  
Sai-Ho Ling
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Fault Detection ◽  
Early Stage ◽  
Fault Detection And Diagnosis ◽  
Training Data ◽  
Support Vector ◽  
Hvac System ◽  
Machine Modeling ◽  
Detection And Diagnosis

The malfunctioning of the heating, ventilating, and air conditioning (HVAC) system is considered to be one of the main challenges in modern buildings. Due to the complexity of the building management system (BMS) with operational data input from a large number of sensors used in HVAC system, the faults can be very difficult to detect in the early stage. While numerous fault detection and diagnosis (FDD) methods with the use of statistical modeling and machine learning have revealed prominent results in recent years, early detection remains a challenging task since many current approaches are unfeasible for diagnosing some HVAC faults and have accuracy performance issues. In view of this, this study presents a novel hybrid FDD approach by combining random forest (RF) and support vector machine (SVM) classifiers for the application of FDD for the HVAC system. Experimental results demonstrate that our proposed hybrid random forest–support vector machine (HRF–SVM) outperforms other methods with higher prediction accuracy (98%), despite that the fault symptoms were insignificant. Furthermore, the proposed framework can reduce the significant number of sensors required and work well with the small number of faulty training data samples available in real-world applications.

A Hybrid Method for Detection and Diagnosis of Faulty Roller Bearings

2019 ◽  
Vol 795 ◽  
pp. 473-478 ◽  
Author(s):  
Fei Xu ◽  
Zhan Si Jiang ◽  
Hui Jiang
Keyword(s):  
Hybrid Method ◽  
Roller Bearing ◽  
Fault Detection And Diagnosis ◽  
Variable Speed ◽  
Bearing Faults ◽  
Order Tracking ◽  
Mode Decomposition ◽  
Detection And Diagnosis ◽  
Effective Diagnosis ◽  
Envelope Spectrum

There are few research on bearing fault detection and diagnosis which combine constant speed with variable speed. In this paper, a hybrid method is proposed to realize the nondestructive testing and fault diagnosis of roller bearing faults. First, adopts computed order tracking (COT) and variational mode decomposition (VMD) based order spectrum method to detect bearing faults under variable speed condition. Then, by using the method of VMD-based envelope spectrum analysis, we analyze a certain speed signal which is consistent with the velocity in the condition of variable speed, and verify the analysis under the condition of variable speed. The experimental results show that the method can realize the effective diagnosis of the fault bearing.

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