Vibration Analysis using Time Domain Methods for the Detection of small Roller Bearing Defects

The analysis of vibration signals is a major technique for monitoring the condition of machine components.

A focus of this paper is given to the early detection of very small bearing damages like false brinelling faults,

which occur in the presence of a small relative motion between the rollers and raceways during non-rotation

times. This leads to a small damage which is characterized by elliptical wear marks in the axial direction at each roller position. The paper shows that the vibration structure generated by a small surface defect differs from a normal state even if the signal energy is eliminated by normalisation of the data. Suitable time domain features are a mathematical description of the shape of selected time domain peaks, which could easily be calculated by the higher derivatives of the time acceleration signal and some parameters characterizing the randomness of the peak positions. After the step of extracting 32 features from the time signal a feature selection process is executed automatically. This enables the selection of a feature subset which is best suited to the present fault situation. Test rig results indicate the high potential of the new time domain features for both fault types. The last chapter gives a short introduction in an algorithm for bearing fault simulation.

Condition monitoring techniques have the objective of achieving the most effective, safe and efficient operation of mechanical plant, machines or engines. In recent years there has been an increasing interest due to the requirement of reduced maintenance costs, improved productivity and safety. Roller bearings are used in a wide range in industrial rotating machinery and the robustness and reliability of roller bearings are essential for the machine health. Damages can put human safety at risk, cause long term machine down times, interruption of production and result in high costs. Main steps in condition monitoring are applying measurement techniques,signal processing and signal categorisation combined with classification algorithms, see and The healthy signature can be measured on operating machines but data with seeded faults are more difficult to obtain.This paper presents an approach for detection of roller bearing defects using time domain methods, without the necessity of special information about bearing type and other operating parameters. The vibration signals of a roller bearing deliver a large content of information about its structural dynamics and operating conditions.Typical representatives as measurement parameters are displacement, velocity and acceleration.

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