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Signal Processing [clear filter]
Wednesday, May 15
 

11:00am

CANCELLED An Octave Analysis Approach Based on Vibrational Data for Early Detection of Muliple Faults in Rolling Bearing
The mechanical industry is focusing on modern maintenance techniques to minimize the downtime so that the production can be increased. The fourth industrial revolution also known as Industry 4.0 is aiming to provide fully autonomous systems including condition monitoring fault diagnostics techniques. In order to implement the Industry 4.0, the maintenance department needs automatic condition monitoring and fault diagnostics techniques. In this study, octave analysis has been proposed for early detection of bearing faults. In order to show the effectiveness of the proposed methodology, detailed experimentations have been performed on a Machinery Fault Simulator (MFS). The octave analysis has been implemented on the vibration data acquired through the accelerometers placed on the inboard and outboard bearing housing. The vibration data of a healthy bearing has been compared with a faulty bearing at four different rotational speeds under two operating conditions: no load applied and a constant load applied near outboard bearing housing. The standard deviation has also been calculated for healthy and faulty bearings. The octave analysis has high peaks for all rotational speeds and both operating conditions. The values of standard deviation of faulty bearings were found to be high as compared to a healthy bearing. This research article proposes a simple approach that does not require signal processing. The results proved that octave analysis along with standard deviation can be used for early detection of rolling bearing faults.
Keywords: Condition Monitoring, Fault Detection, Industry 4.0, Octave Analysis, Rolling Bearings, Vibrations.

Speakers
NA

Naqash Azeem

Northwestern Polytechnical University


Wednesday May 15, 2019 11:00am - 11:30am
Freedom Ballroom I

11:30am

Intelligent Fault Identification of Planet Bearings Using Discriminative Dictionary Learning Based Sparse Representation Classification Framework
Planet bearing fault identification is an attractive but challenging task in numerous engineering applications, such as wind turbine and helicopter transmission systems. However, traditional fault characteristic frequency identification and impulsive feature extraction based diagnosis strategies are not sufficient to resolve the problem of planet bearing fault detection, due to complex physical configurations and modulation characteristics in planetary gearboxes. In this paper, a novel discriminative dictionary learning based sparse representation classification (SRC) framework is proposed for intelligent planet bearing fault identification. Within our approach, the optimization objective for discriminative dictionary learning introduces a label consistent constraint called discriminative sparse code error™ and incorporates it with the reconstruction error and classification error to bridge the gap between the classical dictionary learning and classifier training. Therefore, not only the reconstructive and discriminative dictionary for signal sparse representation but also an optimal universal multiclass classifier for classification tasks could be simultaneously learnt in the proposed framework. The optimization formulation could be efficiently solved using the well-known K-SVD dictionary learning algorithm. The effectiveness of the proposed framework has been validated using experimental planet bearing vibration signals. Comparative results demonstrate that our framework outperforms the state-of-the-art K-SVD based SRC method in terms of classification accuracy for intelligent planet bearing fault identification.

Speakers
avatar for Yun Kong

Yun Kong

Dr., Tsinghua University


Wednesday May 15, 2019 11:30am - 12:00pm
Freedom Ballroom I