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Research Topics

Prognostic Health Monitoring (PHM) of mechanical systems:

Detection of damaged mechanical components in their early stages is crucial in many applications. The diagnostics of mechanical components is achieved most effectively using vibration and/or acoustical measurements, sometimes accompanied by oil debris indications.

Vibration monitoring can be used to detect machine faults, including: unbalance, misalignment, oil film bearing instabilities, roller bearing degradation, gear damages, mechanical looseness, structural resonance, and cracked rotors. It is applicable to a range of industrial machines such as energy turbines, engines, helicopters, ships or land vehicles.

Several research topics fall within the domain of vibration diagnostics. As of today, the implementation of condition based maintenance (CBM) based on vibration monitoring is still difficult. The reason is that many expensive tests and large amounts of data are needed in order to develop the correct algorithm to identify and prevent a certain failure mode. 


We are doing research in three main areas:


  1. Modeling: Constructing a reliable analytical and numerical model to study the physical behavior of the mechanical system with the damage.

  2. Gears: Developing condition indicators for gears

  3. Prognostic model: Developing tools to calculate the amount of deterioration of the mechanical component based on vibration analysis.


Development of “Smart” bearing: 


The overall goal of this research is to develop and manufacture a bearing able to autonomously defend a mechanical system from cyber-attacks. Three specific aims are addressed: The first is to investigate the feasibility of developing an algorithm, which will identify that the mechanical system is under attack, by analyzing operation parameters of the bearing. The second is to develop an internal mechanism in the bearing, implementing the algorithm developed in the first aim. Aim three is to develop an internal mechanism in the bearing, which will allow a mechanical separation of the system's critical components in case of a cyber-attack. The methods of the first aim will be based on the methods used today in monitoring the health of mechanical systems, a.k.a. HUMS method, using signal processing and pattern recognition. Briefly, we will characterize the different signatures of different modes of action, in order to clearly identify signatures indicating deviation from routine actions (Second grant above).



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