Characterization of Acoustic Emissions from Mechanical Seals for Fault Detection

The application of high-frequency Acoustic Emissions (AE) for mechanical seals diagnosis is gaining acceptance as a useful complimentary tool. This paper investigates the AE characteristics of mechanical seals under different rotational speed and fluid pressure (load) for develop a more comprehensive monitoring method. A theoretical relationship between friction in asperity contact and energy of AE signals is developed in present work. This model demonstrates a clear correlation between AE Root Mean Square (RMS) value and sliding speed, contact load and number of contact asperities. To benchmark the proposed model, a mechanical seal test rig was employed for collecting AE signals under different operating conditions. Then, the collected data was processed using time domain and frequency domain analysis methods to suppressing noise interferences from mechanical system for extracting reliably the AE signals from mechanical seals. The results reveal the potential of AE technology and data analysis method applied in this work for monitoring the contact condition of mechanical seals, which will be vital for developing a comprehensive monitoring systems and supporting the optimal design and operation of mechanical seals

Journal bearing lubrication monitoring based on spectrum cluster analysis of vibration signals

Journal bearings are critical components for many important machines. Lubrication analysis techniques are often not timely and cost effective for monitoring journal bearings. This research investigates into vibration responses of such bearings using a clustering technique for identifying different lubrication regimes, and consequently for assessing bearing lubrication conditions. It firstly understands that the vibration sources are mainly due to the nonlinear effects including micro asperity collisions and fluid shearing interactions. These excitations together with complicated vibration paths are difficult to be characterized in a linear way for the purpose of condition monitoring. Therefore, a clustering analysis technique is adopted to classify the vibration spectrum in high frequency ranges around 10kHz into different representative responses that corresponds to different bearing modulus values and lubrication characteristics. In particular, the analysis allows sensitive signal components and sensor positions to be determined for monitoring the journal bearing effectively. Test results from self-aligning spherical journal bearings show that it allows different lubricant oils and different lubrication regimes to be identified appropriately, providing feasible ways to online monitoring bearing conditions

Condition Monitoring of Journal Bearings for Predictive Maintenance Management Based on High Frequency Vibration Analysis

Journal bearings are widely used as rotor supports in many machinery systems such as engines, motors, turbines and huge pumps. The journal bearing is simply designed, highly efficient, has a long life, low cost and doesn’t fail easily. Based on preventive maintenance strategies, many monitoring techniques are developed for monitoring journal bearings such as lubricant analysis, vibration analysis, noise and acoustic emission analysis. Vibration monitoring techniques have been developed and it can be implemented online or offline without interrupting the machine operations. The vibration phenomena in a journal bearing is complicated which combined between different types of signals created by different sources. To understand this phenomenon, a vibration model is established for fault diagnosis, which includes not only conventional hydrodynamic forces but also excitations of both asperity collisions and churns. However, mis-operations and oil degradation in the journal bearings might cause unexpected and sudden failure which is risky in machines and operators. Consequently, clustering technique is used to investigate into vibration responses of journal bearings for identifying different lubrication regimes as categorised by the classic Stribeck curve. High frequency clustering allows different lubricant oils and different lubrication regimes to be identified appropriately, providing feasible ways for online monitoring of bearing conditions. Additionally, modulation signal bispectrum magnitude results represent the nonlinear vibration responses with two distinctive bifrequency patterns corresponding to instable lubrication and asperity interactions. Using entropy measures, these instable operating conditions are classified to be the low loads cases. Furthermore, average MSB magnitudes are used to differentiate the asperity interactions between asperity collisions and the asperity churns. In addition, the oil starvation of a journal bearing has been found by MSB analysis that the instable frequency can affect the measured vibration responses. Moreover, the structural resonances in the high frequency range can better reflect the separation of different oil levels under wide operating conditions. Finally, As a result of worn bearings, shaft fluctuation increases and asperity collisions decreases. Thus a worn bearing is not all the time good because of instability

Monitoring Oil Levels Of Journal Bearings Based On The Analysis Of Vibration Signals

This paper presents a study of monitoring the oil starvation of a journal bearing based on vibration analysis. A diagnostic model is established by includ-ing asperity ploughs and collisions. These excitations are more significant as the oil level is reduced due to less oil film effect. However, it has been found by modulation signal bispectrum analysis that the instable oil whirls can affect the measured responses in the middle frequency range (3.5kHz to 5.5kHz), leading to a good detection of the instability but an inconsistent diagnosis. However, the structural resonances in the high frequency range (5.5kHz to 11kHz) can better reflect the excitations and result in a more agreeable separation of different levels under wide operating conditions

Different Signal Processing Techniques for Predicting the Condition of Journal Bearings

Condition monitoring is based on the idea that by monitoring the behaviour of an asset within its operating environment and analysing the information and data. CM identifies pre-failure symptoms, spot trends in deterioration and even makes predictions on when and how the item is likely to fail [1].The main purpose of condition monitoring is to detect, diagnose and prognoses a fault, or a degradation process, that has reached a certain symptomatic level and to provide an indication of the abnormality in time before the functional breakdown occurs [2].In addition, monitoring is the most important strategy to diagnose the faults before the plants failure. Vibration monitoring is the most commonly used and effective technique to detect internal defects in rotating machinery. Captured vibration signal data used to detect a fault in self-aligning journal bearing, or a degradation process, that has reached a certain symptomatic level and to provide an indication of the abnormality in time before the break occurs. The optimal condition parameters which produces high amplitude vibration signatures sensitive to the operating processes of self-aligning journal bearing are under high radial load (20 bars), at high motor speed (100 %) and with low viscosity lubricant (oil 32). Because of many different vibration sources, measured data from a self-aligning bearing need to be preprocessed to eliminating such influences and obtain optimal parameters to represent the dynamics of the bearing. In general, time domain and frequency domain analyses are used to process vibration signals for the purpose of effective feature attraction. Because of limitations of time and frequency domain, STFT has been developed for non-stationary signals which are also common when machinery faults occur to investigate waveform signal in both time and frequency domain at same time

Journal bearing condition monitoring based on the modulation signal bispectrum analysis of vibrations

Journal bearings usually wok under a wide range of operating conditions. However, adverse operating such as transient operations and oil degradation can lead to early defects to the bearings. In this paper, modulation signal bispectrum (MSB) is used to analyse vibration responses from a journal bearing lubricated with three different oils to differentiate abnormal lubrication conditions. MSB magnitude results represent the nonlinear vibration responses, which are due to instable hydrodynamics, asperity excitations and nonlinear transfer paths, with two distinctive bifrequency patterns corresponding to instable lubrication and asperity interactions respectively. Using entropy measures, these instable lubrications are classified to be the low loads cases. Furthermore, average MSB magnitudes are used to differentiate the asperity interactions between asperity collisions and the asperity churns. A higher magnitude in the lower frequency band can indicate the excessive asperity contacts due to lowering viscosities. Meanwhile a higher magnitude in the higher frequency band indicates the extreme fluid frictions

Proceedings of First Conference for Engineering Sciences and Technology: Vol. 2

This volume contains contributed articles of Track 4, Track 5 & Track 6, presented in the conference CEST-2018, organized by Faculty of Engineering Garaboulli, and Faculty of Engineering, Al-khoms, Elmergib University (Libya) on 25-27 September 2018. Track 4: Industrial, Structural Technologies and Science Material Track 5: Engineering Systems and Sustainable Development Track 6: Engineering Management Other articles of Track 1, 2 & 3 have been published in volume 1 of the proceedings at this lin