An advanced real-time predictive maintenance framework for large scale machine systems

This thesis introduces and develops a novel real-time predictive maintenance system to estimate the machine system parameters using the motion current signature. Recently, motion current signature analysis has been addressed as an alternative to the use of sensors for monitoring internal faults of a motor. A maintenance system based upon the analysis of motion current signature avoids the need for the implementation and maintenance of expensive motion sensing technology. By developing nonlinear dynamical analysis for motion current signature, the research described in this thesis implements a novel real-time predictive maintenance system for current and future manufacturing machine systems. A crucial concept underpinning this project is that the motion current signature contains infor­mation relating to the machine system parameters and that this information can be extracted using nonlinear mapping techniques, such as neural networks. Towards this end, a proof of con­cept procedure is performed, which substantiates this concept. A simulation model, TuneLearn, is developed to simulate the large amount of training data required by the neural network ap­proach. Statistical validation and verification of the model is performed to ascertain confidence in the simulated motion current signature. Validation experiment concludes that, although, the simulation model generates a good macro-dynamical mapping of the motion current signature, it fails to accurately map the micro-dynamical structure due to the lack of knowledge regarding performance of higher order and nonlinear factors, such as backlash and compliance. Failure of the simulation model to determine the micro-dynamical structure suggests the pres­ence of nonlinearity in the motion current signature. This motivated us to perform surrogate data testing for nonlinearity in the motion current signature. Results confirm the presence of nonlinearity in the motion current signature, thereby, motivating the use of nonlinear tech­niques for further analysis. Outcomes of the experiment show that nonlinear noise reduction combined with the linear reverse algorithm offers precise machine system parameter estimation using the motion current signature for the implementation of the real-time predictive maintenance system. Finally, a linear reverse algorithm, BJEST, is developed and applied to the motion current signature to estimate the machine system parameters.

What's in a broken promissory obligation? Developing and testing a multiple component form measure of psychological contract breach

While the literature has suggested the possibility of breach being composed of multiple facets, no previous study has investigated this possibility empirically. This study examined the factor structure of typical component forms in order to develop a multiple component form measure of breach. Two studies were conducted. In study 1 (N = 420) multi-item measures based on causal indicators representing promissory obligations were developed for the five potential component forms (delay, magnitude, type/form, inequity and reciprocal imbalance). Exploratory factor analysis showed that the five components loaded onto one higher order factor, namely psychological contract breach suggesting that breach is composed of different aspects rather than types of breach. Confirmatory factor analysis provided further evidence for the proposed model. In addition, the model achieved high construct reliability and showed good construct, convergent, discriminant and predictive validity. Study 2 data (N = 189), used to validate study 1 results, compared the multiple-component measure with an established multiple item measure of breach (rather than a single item as in study 1) and also tested for discriminant validity with an established multiple item measure of violation. Findings replicated those in study 1. The findings have important implications for considering alternative, more comprehensive and elaborate ways of assessing breach.

Can psychometric testing be used to explain, predict and measure behavioural competences and the funding resource orchestration of the entrepreneur?

Financing is a critical entrepreneurial activity (Shane et al. 2003) and within the study of entrepreneurship, behaviour has been identified as an area requiring further exploration (Bird et al. 2012). Since 2008 supply side conditions for SMEs have been severe and increasingly entrepreneurs have to bundle or ‘orchestrate’ funding from a variety of sources in order to successfully finance the firm (Wright and Stigliani 2013: p.15). This longitudinal study uses psychometric testing to measure the behavioural competences of a panel of sixty entrepreneurs in the Creative Industries sector. Interviews were conducted over a 3 year period to identify finance finding behaviour. The research takes a pragmatic realism perspective to examine process and the different behavioural competences of entrepreneurs. The predictive qualities of this behaviour are explored in a funding context. The research confirmed a strong behavioural characteristic as validated through interviews and psychometric testing, was an orientation towards engagement and working with other organisations. In a funding context, this manifested itself in entrepreneurs using networks, seeking advice and sharing equity to fund growth. These co-operative, collaborative characteristics are different to the classic image of the entrepreneur as a risk-taker or extrovert. Leadership and achievement orientation were amongst the lowest scores. Three distinctive groups were identified and also shown by subsequent analysis to be a positive contribution to how entrepreneurial behavioural competences can be considered. Belonging to one of these three clusters is a strong predictive indicator of entrepreneurial behaviour – in this context, how entrepreneurs access finance. These Clusters were also proven to have different characteristics in relation to funding outcomes. The study seeks to make a contribution through the development of a methodology for entrepreneurs, policy makers and financial institutions to identify competencies in finding finance and overcome problems in information asymmetry.