Condition monitoring of gears using transmission error

Condition monitoring is crucial for safe and economic machine operations. Although vibration dominates gear condition monitoring and has been undeniably proven successful in early fault detection, its effectiveness in fault severity assessment is still debated. Disadvantages of vibration in such applications include remoteness from the gears, a complex transfer path, insensitivity to average wear, masking by other sources, and strong dependence on operating conditions. Solutions are available, but usually require complex gear models and/or large datasets for the training of data-driven methods. An underappreciated alternative to vibration is gear transmission error (TE), based on angular encoder measurements of the gear shafts. The more direct connection of the sensors to the gears suggests a much easier conversion of TE into a micrometre measurement of gear wear and tooth deflections. However, the lack of scientific analysis of TE leaves three key challenges. Firstly, like vibration, current TE measurements lack an absolute reference and are therefore insensitive to average wear. Secondly, despite being much simpler than that of vibration, a transfer path still exists between the gear mesh and measurements, affecting accuracy at higher operating speeds. Finally, profile-error and tooth-deflection components must be separated from within TE to allow for their correlation with wear and cracks, respectively. This thesis aims to address these gaps to enable the effective use of TE in gear condition monitoring through several innovations. Firstly, a procedure was proposed to obtain “absolute TE”, which includes information on average wear depth. Then, a technique was developed for the removal of transfer-path effects, extending the applicability of TE to higher speeds. Finally, the latter was integrated within a broader method, able to separate wear- and crack-related components and automatically estimate crack severity. To support this work, a vast set of unique experimental wear and crack tests were conducted, providing new insights on these faults and their impact on TE. These developments are not separate, and form a coherent strategy for the use of TE in fault severity assessment. Its accurate, reliable and physically justified results constitute a crucial resource for future work in the prognostics and health management of gears

Prognostic-based Life Extension Methodology with Application to Power Generation Systems

Practicable life extension of engineering systems would be a remarkable application of prognostics. This research proposes a framework for prognostic-base life extension. This research investigates the use of prognostic data to mobilize the potential residual life. The obstacles in performing life extension include: lack of knowledge, lack of tools, lack of data, and lack of time. This research primarily considers using the acoustic emission (AE) technology for quick-response diagnostic. To be specific, an important feature of AE data was statistically modeled to provide quick, robust and intuitive diagnostic capability. The proposed model was successful to detect the out of control situation when the data of faulty bearing was applied. This research also highlights the importance of self-healing materials. One main component of the proposed life extension framework is the trend analysis module. This module analyzes the pattern of the time-ordered degradation measures. The trend analysis is helpful not only for early fault detection but also to track the improvement in the degradation rate. This research considered trend analysis methods for the prognostic parameters, degradation waveform and multivariate data. In this respect, graphical methods was found appropriate for trend detection of signal features. Hilbert Huang Transform was applied to analyze the trends in waveforms. For multivariate data, it was realized that PCA is able to indicate the trends in the data if accompanied by proper data processing. In addition, two algorithms are introduced to address non-monotonic trends. It seems, both algorithms have the potential to treat the non-monotonicity in degradation data. Although considerable research has been devoted to developing prognostics algorithms, rather less attention has been paid to post-prognostic issues such as maintenance decision making. A multi-objective optimization model is presented for a power generation unit. This model proves the ability of prognostic models to balance between power generation and life extension. In this research, the confronting objective functions were defined as maximizing profit and maximizing service life. The decision variables include the shaft speed and duration of maintenance actions. The results of the optimization models showed clearly that maximizing the service life requires lower shaft speed and longer maintenance time

The application of time encoded signals to automated machine condition classification using neural networks

This thesis considers the classification of physical states in a simplified gearbox using acoustical data and simple time domain signal shape characterisation techniques allied to a basic feedforward multi-layer perceptron neural network. A novel extension to the signal coding scheme (TES), involving the application of energy based shape descriptors, was developed. This sought specifically to improve the techniques suitability to the identification of mechanical states and was evaluated against the more traditional minima based TES descriptors. The application of learning based identification techniques offers potential advantages over more traditional programmed techniques both in terms of greater noise immunity and in the reduced requirement for highly skilled operators. The practical advantages accrued by using these networks are studied together with some of the problems associated in their use within safety critical monitoring systems.Practical trials were used as a means of developing the TES conversion mechanism and were used to evaluate the requirements of the neural networks being used to classify the data. These assessed the effects upon performance of the acquisition and digital signal processing phases as well as the subsequent training requirements of networks used for accurate condition classification. Both random data selection and more operator intensive performance based selection processes were evaluated for training. Some rudimentary studies were performed on the internal architectural configuration of the neural networks in order to quantify its influence on the classification process, specifically its effect upon fault resolution enhancement.The techniques have proved to be successful in separating several unique physical states without the necessity for complex state definitions to be identified in advance. Both the computational demands and the practical constraints arising from the use of these techniques fall within the bounds of a realisable system

Mechanical Engineering

The book substantially offers the latest progresses about the important topics of the "Mechanical Engineering" to readers. It includes twenty-eight excellent studies prepared using state-of-art methodologies by professional researchers from different countries. The sections in the book comprise of the following titles: power transmission system, manufacturing processes and system analysis, thermo-fluid systems, simulations and computer applications, and new approaches in mechanical engineering education and organization systems

AGBT Advanced Counter-Rotating Gearbox Detailed Design Report

An Advanced Counter-Rotating (CR) Gearbox was designed and fabricated to evaluate gearbox efficiency, durability and weight characteristics for emerging propfan-powered airplanes. Component scavenge tests showed that a constant volume collector had high scavenge effectiveness, which was uneffected by added airflow. Lubrication tests showed that gearbox losses could be reduced by controlling the air/oil mixture and by directing the oil jets radially, with a slight axial component, into the sun/planet gears

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Influence of centrifugal compressor system components on its general rotordynamic characteristics

Nowadays most countries are depending on Oil and Gas for their energy supply. In such operations, centrifugal compressors are dominating most of the used critical machines hence it is important to give these turbomachines more consideration in terms of their technical performance and reliability. Centrifugal compressors are one of many turbomachines that require technical solutions for Enhanced Oil Recovery (EOR). The oil and gas fields have different production environments which require adequate selection of compressors to handle the variance in gas and oil specifications and this in turn force the equipment manufacturers to revise their currently used design specifications. This research presents different types of compressors and their work principles with an emphasis on centrifugal compressor components The literature review carried in this research describes different cases in turbomachinery rotordynamics where failures were encountered at the commissioning and operation stages. Also the literature shows how these machines are improved technically by improving the compressor components performance such using Pocket Damper seals and tilting type bearings. The aim of this research is to study the factors affecting Rotordynamic behaviour of large natural gas centrifugal compressors. The study will review the influence of various conditions of rotor components such as bearings, seals, impellers, etc on the overall Rotordynamic stability at various process conditions ... [cont.]

Gear error induced impact in a multiple take-off textile drive system operating under light loading

A combined theoretical and experimental investigation is made into the causes of premature gear failures which had occurred in the complex and lighly loaded gear trains of an industrial textile machine. A comprehensive review of previous relevant work identifies the problem as one of torsional vibratory impact excited by gear transmission errors. An extension of the dynamic stiffness method is developed for the analysis of forced vibration response due to relative displacement, harmonic excitation imparted by transmission error components. The technique is then applied to a generalised mathematical model of the complete machine in which typical production distributions of gear error magnitude and relative phasing are inserted. Gear-tooth dynamic loads are computed at every mesh for a number of different machine configurations over the operating speed range. The influence of selected inertia, flexibility and damping elements is demonstrated. A novel technique employing magnetic drums is devised and evaluated for the direct measurement of-relative motions in a meshed pair of oscillating gears. Automatic compensation is provided for transmission error and mounting eccentricity. A further direct technique is reported for the detection of tooth impacts and is based on the change in electrical resistance between meshing teeth as the contact pressure varies. Measurements in a multi-gearbox experimental rig demonstrated that the gears described non-linear motions, involving excursions through the backlash and heavy impacts on both drive and reverse faces. Theoretical predictibns of dynamic loading distribution within machines show reasonable compatibility with patterns of gear failure recorded in service, even though the analysis does not allow for system non-linearities. Machine design considerations are examined in retrospect from a dynamics standpoint. Past and present designs are appraised and possible alternatives to these are briefly discussed. Finally, the salient factors identified in the investigation are summarised and recommendations made for future work