Advanced Signal Processing Algorithms for Bearing Fault Detection and Diagnosis

Zhenyu Yang

(Cooperated with Grundfos A/S)

A set of sampled current data for an outer raceway fault.

The bearing component plays a critical role in rotational machines, its functionality is directly relevant to the operational performance, and consequently the reliability and safety of these machines and relevant systems. Some investigation discovered that over 52% of induction motor failures are due to bearing component faults. Thereby some cost-effective early Fault Detection and Diagnosis (FDD) of potential bearing faults is very important and necessary when it comes to reliable operation of a given system [1,2].

Fig.1 A photo of a typical roll-element bearing with some defects [1]

One of our previous work [1,3] investigated the fault detection and diagnosis for a class of rolling-element bearings using signal-based methods based on the motor's vibration and phase current measurements, respectively. The envelope detection method is employed to preprocess the measured vibration data before the FFT algorithm is used for vibration analysis. The average of a set of Short-Time FFT (STFFT) is used for the current spectrum analysis.

The experimental results show the powerful capability of vibration analysis in the bearing point-defect fault diagnosis under stationary operation. The current analysis showed a subtle capability in diagnosis of point-defect faults depending on the type of fault, severity of the fault and operational condition. The generalized roughness fault can not be detected by the proposed frequency methods. The temporal features of the considered faults and their impact on the diagnosis analysis are also investigated, more results can be found in [1].

There are some open problems, such as the power supply frequency (50Hz) and its harmonies are still presented in the obtained spectra no matter from the vibration signals or from phase current signals, as shown in Fig.2. Thereby within this project, some band-stop filters (e.g., notch filters) are expected to be developed and used to filter out the obtained data. Furthermore, some advanced signal processing techniques are also expected to enhance the signatures detection due to potential faults.     
 

Fig.2 A typical spectrtum of bearing fault detection (350, 400, 450, 500, 550 Hz are harmonies of 50 Hz power supply) [1]

The project will mainly focus on the investigation of notch filters and some advanced DSP algorithms. The comany provides testing data. All development and analysis  can be done in Matlab/Simuink environment. There is no need to touch any hardwares. 

References

[1] Zhenyu Yang, Uffe C. Merrild, Morten T. Runge, Gerulf Pedersen and Hakon Børsting (2009), “A Study of Rolling-Element Bearing Fault Diagnosis Using Motor's Vibration and Current Signatures”, in Proc. of The 7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes (SAFEPROCESS 2009), Barcelona, Spain, Jun 30-Jul 3, 2009. pp.354-359. (ABB Best Application Award Finalist)

[2] Mohamed El H. Benbouzid, "A review of induction motors signature analysis as a medium for faults detection", IEEE Trans. on Industrial Electronics, 47(5): 984--993, 2000.

[3] Uffe C. Merrild and Morten T. Runge, "Detection of bearing faults in induction motors", Master Thesis, Aalborg University, 2006.
 

 

updated 02.09.09