A review of mechanical seals tribology and condition monitoring

Mechanical seals have become one of the most popular sealing systems for rotating machinery because of low leakage and absence of a requirement for routine maintenance. Generally, a mechanical face seal should operate with a fluid film as thin as possible, to reduce the leakage and to restrict friction and wear. Recent advances in a system of computer software based on finite element modelling and analytical approaches help in understanding of the working conditions of the mechanical face seals. This paper reviews tribological bahavior and condition monitoring of mechanical seals based on the literature of the recent years. It covers friction, wear and thermal characteristics of mechanical seals and the application of computational methods and other techniques to give good understanding of the tribological behavior and condition monitoring of seal faces

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

Identification of lubrication Regimes in Mechanical Seals using Acoustic Emission for Condition Monitoring

The quality of lubrication condition between seal faces directly affects the reliability, operating life and sealing performance of mechanical seals. Thus, the identification of lubrication regimes in face seals i.e. boundary lubrication (BL), mixed lubrication (ML) and hydrodynamic lubrication (HL) is of high importance for developing effective online condition monitoring approaches. This paper investigates the tribological behaviour and frictional characteristics of mechanical seals based on nonintrusive acoustic emission (AE) measurements. Mathematical models for AE generation mechanisms are derived based on the tribological behaviour and operating parameters of mechanical seals. They produce agreeable results with experimental data in explaining the types of AE signals observed in monitoring the face lubrication conditions. Frequency domain analysis of data shows that the viscous friction process generates more low frequency AE signals, whereas the asperity interactions show more high frequency AE. Moreover, the feasibility of using statistical parameters of the time domain data is shown to identify the lubrication regimes in face seals

Investigation of the Nonlinear Tribological Behaviour of Mechanical Seals for Online Condition Monitoring

Mechanical seals have increasingly been used for sealing rotating shafts in centrifugal pumps, propeller shafts in ships and submarines, compressors, liquid propellant rocket motors in aerospace industry, pumps, turbines, mixers and many other rotating machines during last two decades. Abnormal operating conditions in the mechanical seals will degrade machine performance, increase operating cost and may cause unexpected sudden failures which are dangerous in both engineering and safety terms. Hence it is necessary to investigate the tribological behaviour of mechanical seals operating based on nonlinear coupling between fluid and surface dynamics, in order to develop more advanced diagnostic technologies to improve the reliability of such machines operating with mechanical seals. Different condition monitoring techniques have been studied to evaluate the lubrication state and severity of contact between the mating faces in mechanical seals. However, some of them are not cost effective others are not practical in industrial applications. Acoustic emission (AE) has been proved to be a sensitive indicator of lubrication conditions and changes in the lubricant properties, however the application of technique for identification of lubrication regimes in mechanical seals has not been reported yet. Moreover, previous studies give relatively little information to acoustic emission condition monitoring of mechanical seals, nor has comprehensive fault detection been implemented for a particular case. In addition, a review on previous works reveals the lack of comprehensive mathematical models to explain the relationship between AE energy and tribological characteristics of the mating faces under healthy and faulty conditions. In this research, the tribological behaviour of mechanical seals is investigated using acoustic emission measurements to pave a way for fault detection at early stage. Three common seal failures i.e. dry running, spring fault, and defective seal are studied in this thesis. The main objective is to extract AE features that can explain the tribological behaviour of mechanical seals under both healthy and faulty conditions. To achieve this, a purpose-built test rig was employed for collecting AE signals from the mechanical seals. Then, the collected data was processed using time domain, frequency domain and time frequency domain analysis methods which are of the most common techniques used for monitoring in AE applications. Based on results the main frequency band that can present the tribological behaviour of mechanical seals was detected. Also it has been proved that AE features in time domain and frequency domain can be effectively applied to indicate the lubrication condition of mechanical seals as well as early fault detection. Moreover, mathematical models were developed to establish a relationship between AE root mean square (RMS) value of AE signals and working parameters of seals (rotational speed, load and number of asperities in contact) under different lubrication regimes. A good agreement was achieved between measured and predicted signals that gives a good evidence of the effectiveness of proposed models. Especially in case of leakage that is one of the main situations indicating the seal failure, a significant difference was observed between the predicted signal for healthy case and the measured signal under faulty conditions. Therefore, it can be deduced that the AE measurement system and signal processing developed in this work has a promising potential to be used to diagnose and monitor the mechanical seals online. Finally, the conclusions and achievements are given based on the entirety of this research work, and online monitoring incorporating with AE features and mathematical models developed in this thesis are suggested as the main works for further research

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

Comparing the Regression Analysis and Artificial Neural Network in Modeling the Submerged Arc Welding (SAW) Process

Abstract: 6 T Complexities of6 T 6 T submerged arc6 T 6 T welding6 T 6 T variables6 T on the one hand and its widespread use in producing the sensitive and expensive parts on the other hand have doubled the importance of precise control of its adjusting parameters. In general, in order to create high-quality joints in welding processes it is necessary to control three parameters of welding 6 T current6 T , voltage and speed precisely from various variables. On this basis, the mentioned variables have been considered as the criteria for quality of the weld joints in this study as the adjusting parameters and weld bead geometry, which include the bead height, width and penetration. Thus, the accurate equations have been proposed for estimating the weld bead height, width and penetration based on the input parameters by the regression analysis and neural network. Based on the results, the designed neural network is markedly more accurate than the regression equations, but both models have high capabilities for optimizing the parameters of submerged arc welding and also predicting the weld bead geometry for a set of input values

Characterization of Acoustic Emissions from Journal Bearings for Fault Detection

Acoustic emission (AE) technology is one of the most established diagnostic techniques for rolling bearing monitoring in rotating machinery. The application of high-frequency AE for bearing diagnosis is gaining acceptance as a useful complimentary tool. This paper demonstrates the use of AE measurements to investigate the AE characteristics of self-aligning journal bearings under different rotational speed, radial load and lubrication condition. To undertake this task, a purpose-built test rig was employed for collecting AE signals from the journal bearings. Then, the collected data was processed using time domain and frequency domain analysis methods which are of the most common techniques used for monitoring in AE applications. The results shown that the data analysis method applied in this work is effective for characterising complicated AE signals. Based on obtained results, it is concluded that the AE energy levels in high frequency range higher for the higher radial load and speed condition. For different lubricant cases AE energy becomes high when the viscosity is lower, which means that AE can be used to detect lubrication degradation in journal bearings

Successes and challenges in non-destructive testing of aircraft composite structures

Composite materials are increasingly used in the aerospace industry. To fully realise the weight saving potential along with superior mechanical properties that composites offer in safety critical applications, reliable Non-Destructive Testing (NDT) methods are required to prevent catastrophic failures.This paper will review the state of the art in the field and point to highlight the success and challenges that different NDT methods are faced to evaluate the integrity of critical aerospace composites.The focus will be on advanced certificated NDT methods for damage detection and characterization in composite laminates for use in the aircraft primary and secondary structures