MFPT2019 has ended
Mobile App sponsored by Poseidon Systems, LLC

Log in to bookmark your favorites and sync them to your phone or calendar.

Signal Processing [clear filter]
Wednesday, May 15

8:30am EDT

The Case of the Missing Bearing Fault Frequency
During testing on a small gas turbine, frequencies related power turbine shaft and a bearing were observed. The frequencies of the fault were not associated with any known bearing on the power turbine shaft. This paper is an investigation of why the observed bearing fault frequency of 8% lower than anticipated. It will be shown that because the faulted bearing was a worn thrust bearing, the contact angle of the roller element had changed. This is an infrequently observed phenomenon which can lead to a missed fault detection on a critical component. A mitigation strategies for this type of failure is discussed.


Eric Bechhoefer

CEO/Chief Engineer, GPMS Inc

Wednesday May 15, 2019 8:30am - 9:00am EDT
Freedom Ballroom I

9:00am EDT

On Vibration Signature Analysis of Rotor/Shaft Cracks
A shaft/rotor crack is a common defect in rotating machinery and detection of such crack is a very serious matter. A crack could lead to a catastrophic failure of the rotor if not detected in a timely manner. Vibrations analysis is widely used to monitor the health of the rotating machinery. However, vibration signature of a cracked shaft is not clear, although a considerable literature is available on the vibration analysis of rotor cracks. The problem is further complicated in presence of misalignment and unbalance loadings. This paper examines the effects of misalignment and unbalance on vibration signature of shaft cracks with the aim of developing unique condition indicators (CI) for crack detections. It is also important to gain an understanding of difference between the signature of breathing and open rotor cracks. An experimental investigation was carried out by the authors to determine a unique signature of shaft seeded with different levels of crack. The data is presented for shafts supported on rolling as well as fluid film bearings. Time-Frequency techniques are applied to study rotors operating under non-stationary conditions.


Daming Chen

Spectra Quest, Inc.

Wednesday May 15, 2019 9:00am - 9:30am EDT
Freedom Ballroom I

9:30am EDT

A Systematic Processing of a Gearbox Vibration Signal with Defective Rolling Element Bearing
avatar for Suri Ganeriwala

Suri Ganeriwala

Founder and President, SpectraQuest
Dr. Suri Ganeriwala is founder and president of Spectra Quest, Inc. He has over thirty-five years of industrial and academic experience in machinery vibration diagnostics and control, signal processing, and viscoelastic materials characterization. Suri has worked for Philip Morris... Read More →

Wednesday May 15, 2019 9:30am - 10:00am EDT
Freedom Ballroom I

11:00am EDT

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.


Naqash Azeem

Northwestern Polytechnical University

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

11:30am EDT

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.

avatar for Yun Kong

Yun Kong

Dr., Tsinghua University

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