Bearing fault detection and fault size estimation using fiberoptic sensors

Significance 

Numerous novel bearing monitoring and diagnosis techniques have been explored in the last two decades to provide a technique that is capable of picking up an incipient bearing fault. On general terms, this is in line with condition monitoring – a process which involves tracking and assessing of the health of critical machines on the basis of detection, diagnosis and prognosis of failure. Bearings are crucial elements of rotating machinery with their purpose being to reduce friction between rotating parts and also support moving parts. Consequently, they are prone to unexpected failures that can yield irreversible damages.

Some authors suggested the utilization of vibration signals to detect imminent failures, success was achieved but under the condition that the sensor be located as close to the bearing defect as possible. On the other hand, optical fibers have demonstrated promising potential. Specifically, strain sensing, silica-based Fiber Bragg Grating (FBG) sensors that have been widely used in static structural health monitoring of bridges, blades and aeronautical structures. However, regarding the subject matter, little information has been published. In fact, no studies on the bearing diagnosis on strain time series have been reported.

In this context, researchers at Ben-Gurion University of the Negev comprised of Prof. Jacob Bortman and Dr. Shlomi Konforty in collaboration with Dr. Hasib Alian (Israel Air Force), Dr. Uri Ben-Simon (Israel Aerospace Industries), Dr. Renata Klein (R.K. Diagnostics) and Dr. Moshe Tur at the Tel Aviv University investigated the capabilities of FBG sensors to monitor bearing defects, including the determination of their sizes. The proposed system was based on the advantage and the ability of the FBG to provide a signal whose contents were mostly determined by the bearing-induced mechanical effects, rather than being influenced by remotely-induced irrelevant and disturbing sources of mechanical noise. Their work is currently published in the research journal, Mechanical Systems and Signal Processing.

In brief, they started by providing a brief description of the FBG sensor following which the experimental setup, test setup, position of the fibers and the defect locations were clearly elucidated. The team then presented test results that included a comparison between healthy and faulty bearings, as well as the techniques for the detection of spalls, and a method to measure the spall width using the FBG signals was proposed and validated.

The Israeli scientists demonstrated that the FBG sensors could be placed in the immediate proximity of bearings, thereby considerably enhancing the strain signal generated by the bearing through an improved signal-to-noise ratio and a reduction of remote influences: local strain was measured rather than the combined local and global acceleration, as in standard vibration measurements.

In summary, a comprehensive pioneering research on diagnostics of bearings by strain measured using FBG fiber optical sensors was presented by Jacob Bortman and his colleagues. Overall, all housing measurement points provided acceptable sensitivity, which was found to be inversely proportional to the geometric distance between the sensor and the fault being indifferent to global, remotely-induced vibrations of the system. Altogether, fiber-optic sensors appear to have promising diagnostic potential for spall-like faults in both the outer and inner races of ball bearings with a very good discrimination power.

About the author

Prof. (Brig. Gen. ret.) Jacob Bortman

Date and place of birth: Israel, Tel Aviv, 30 May 1959
Department of Mechanical Engineering, Ben- Gurion University of the Negev
Tel: + 972-8-6477090, Mobile: + 972-54-9473007, E-mail: [email protected]

Doctor of Science from Washington University in Mechanical Engineering

Short Employment History

2010-present – Full Professor, Department of Mechanical Engineering, Ben-Gurion University of the Negev
2010-present – Chairman and member of several board of directors.
1982-2009 – Material Directorate of the Israel Air Force (including: Commander of “Base 108” – IAF’s Electronic Depot, Head of UAV and Space Department, Head of Aircraft Department and Head of Material Directorate).

Membership in professional/scientific societies: ISIG – Israel Structural Integrity Group, ESIS – European Structural Integrity Society, IACMM – Israel Association for Computational Methods in Mechanics, BINDT – The British Institute of Non-Destructive Testing, Israeli Organization for PHM – Establishing the Israeli group for PHM., Editorial Board member of: “Journal of Mechanical Science and Technology – Advances

Refereed articles in scientific journals and in international conferences: over 80 papers

About the author

Dr. Renata Klein received her B.Sc. in Physics and Ph.D. in the field of Signal Processing from the Technion, Israel Institute of Technology. In the first 17 years of her professional career, she worked in ADA-Rafael, the Israeli Armament Development Authority, where she managed the Vibration Analysis department. Since then, she focused on development of vibration based health management systems for machinery. She invented and managed the development of vibration based diagnostics and prognostics systems that are used successfully in combat helicopters of the Israeli Air Force, in UAVs and in commercial jet engines.

Renata is a lecturer in the faculty of Aerospace Engineering of the Technion, and in the faculty of Mechanical Engineering in Ben Gurion University of the Negev. Renata is the CEO and owner of R.K. Diagnostics, providing R&D services and algorithms to companies who wish to integrate Machinery Health diagnostics and prognostics capabilities in their products. In the recent years, she has supervised MSc and PhD students and co-managed the PHM Lab in Ben Gurion University of the Negev, jointly with Prof. Jacob Bortman.

Reference

Hasib Alian, Shlomi Konforty, Uri Ben-Simon, Renata Klein, Moshe Tur, Jacob Bortman. Bearing fault detection and fault size estimation using fiberoptic sensors. Mechanical Systems and Signal Processing, volume 120 (2019) page 392–407.

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