Condition based maintenance is becoming increasingly popular in many industrial contexts, offering substantial savings and minimising accidental damage. When applied to rotating machinery, its most common tool is vibration analysis, which relies on well-established mathematical models rooted in the theory of cyclo-non-stationary processes. However, the extraction of diagnostic information from the real world vibration signals is a delicate task requiring the application of sophisticated signal processing techniques, tailored for specific machines operating under restricted conditions. Such difficulty in the current state of the art of vibration analysis forces the industry to apply methods with reduced diagnostic capabilities but higher adaptability. However in doing so most of the potential of vibration analysis is lost and advanced techniques become of use only for academic endeavours. The aim of this document is to reduce the gap between industrial and academic applications of condition monitoring, offering ductile and automated tools which still show high detection capabilities. Three main lines of research are presented in this document. Firstly, the implementation of stochastic resonance in an electrical circuit to enhance directly the analog signal from an accelerometer, in order to lower the computational requirements in the next digital signal processing step. Secondly, the extension of already well-established digital signal processing techniques, cepstral prewhitening and spectral kurtosis, to a wider range of operating conditions, proving their effectiveness in the case of non-stationary speeds. Thirdly, the main contribution of the thesis: the introduction of two novel techniques capable of separating the vibrations of a defective component from the overall vibrations of the machine, by means of a threshold in the amplitude spectrum. After the separation, the cyclic content of the vibration signal is extracted and the thresholded signals provide an enhanced detection. The two proposed methods, phase editing and amplitude cyclic frequency decomposition, are both intuitive and of low computational complexity, but show the same capabilities as more sophisticated state of the art techniques. Furthermore, all these tools have been successfully tested on numerically simulated signals as well as on real vibration data from different machinery, lasting from laboratory test rigs to wind turbines drive-trains and aircraft engines. So in conclusion, the proposed techniques are a promising step toward the full exploitation of condition based maintenance in industrial contexts.

Many people use the train both as a daily means of transport as well as a working place to carry out activities such as reading or writing. There are, however, several important factors in this environment that will hamper good performance of such activities. Some of the main sources of disturbance, apart form other train passengers, are noise and vibrations generated from the train itself. Although there are standards available for evaluation of ride comfort in vehicles none of them consider the effects that vibrations have on particular passengers' activities. To address these issues, three different studies were conducted to investigate how low frequency lateral vibrations influence the passengers' ability to read and write onboard trains. The first study was conducted on three types of Inter-Regional trains during normal service and included both a questionnaire survey and vibration measurements. Two proceeding laboratory studies were conducted in a train mock-up where the perceived difficulty of reading and writing was evaluated for different frequencies and amplitudes. To model and clarify how vibrations influence the processes of reading and writing the fundamentals of Human Activity Theory was used as a framework in this thesis. In the field study about 80% of the passengers were found to be reading at some point during the journey, 25% were writing by hand, and 14% worked with portable computers. The passengers applied a wide range of seated postures for their different activities. According to the standardised measurements, even the trains running on poor tracks showed acceptable levels of vibration. However, when the passengers performed a short written test, over 60 % reported to be disturbed or affected by vibrations and noise in the train. In the laboratory studies it was found that the difficulty in reading and writing is strongly influenced by both vibration frequency and acceleration amplitude. The vibration spectra of real trains were found to correspond well to the frequency characteristics of the rated difficulty. It was also observed that moderate levels of difficulty begin at fairly low vibration levels. Contextual parameters like sitting posture and type of activity also showed strong influence on how vibrations cause difficulty.