In condition monitoring (CM) of mechanical drives, the analysis of various physical and chemical properties of the operating lubricant can be used to diagnose defects and assess the state of the system. Recent developments in on-line oil condition sensors and advances in signal processing methods have allowed for a system for on-line oil analysis to be developed and applied in the field of predictive maintenance. The System for On-line Oil Analysis (SOOA) has the ability to measure multiple oil properties of interest and detect faults induced by transients in the acquired signals. Transient detection is based on the cumulative sum of errors (CUSUM) technique, where the error represents the difference between the predicted reference value and the current measured value. Detection of abnormal behaviour, based on transient detection, is followed by fault diagnosis, through integrated assessment of oil properties in real time. The system can operate as a standalone unit with an independent user interface or as a part of a complete integrated diagnostic system, merging oil condition evaluation with vibrational analysis and other techniques. This paper focuses on the algorithms within SOOA in charge of transient detection and fault diagnosis. The results of SOOA operation are presented through a demonstration of the method in a laboratory environment with two different sets of tests: gear pitting and water contamination.

A novel diagnosis technology combining the benefits of spectral kurtosis and wavelet transform is proposed and validated for early defect diagnosis of rolling element bearings. A systematic procedure for feature calculation is proposed and rules for selection of technology parameters are explained. Experimental validation of the proposed method carried out for early detection of the inner race defect. A comparison between frequency band selection through wavelets and spectral kurtosis is also presented. It has been observed that the frequency band selected using spectral kurtosis provide better separation between healthy and defective bearings compared to the frequency band selection using wavelet. In terms of Fisher criterion the use of spectral kurtosis has a gain of 2.75 times compared to the wavelet.

Oil analysis has proven to be an effective tool to assess the condition of mechanical drives and of the lubricant itself. Analysis of the various physical and chemical properties of oil, such as viscosity, temperature, water saturation and wear debris, can be used to determine faulty components or register potentially harmful events. An automated online oil analysis system has been designed to simultaneously measure and detect changes in critical oil properties. Based on that, it is able to pinpoint the illfated components and degradation of the lubricant, crosscontamination, excessive temperatures, water/contaminants ingression and the severity of wear. By establishing a model of the machine life-cycle, the system is able to follow its evolution and detect deviations from healthy behaviour. It provides the operators with the qualitative characterisation of status signals ('Good', 'Warning' and 'Critical') and final inference on component condition. A demonstration of the laboratory test machine is presented. Figure 1. Scheme of the automated online oil analysis system Figure 8. Results of the water contamination: (a) graph of the relative moisture content and QT evaluation; (b) fault probability

Purpose – The purpose of this paper is to present a data fusion methodology for online oil condition and wear particles monitoring for assessment of a mechanical spur gear transmission system. Design/methodology/approach – In this work, a background understanding of the tribological phenomena behind oil degradation and wear on the contact surface of mechanical elements is presented. Experimental results were obtained from oil continuously sampled from an operating a single-stage gearbox. Sampling was done by a multi-sensor automated prototype and online analysis performed by algorithms implemented in a C-code programmed graphical user interface. Findings – Two sets of experiments were performed to observe different fault events frequently occurred in an industrial environment. Fault detection was achieved in appropriate time under constant operating conditions. Under variable operating conditions, same results were obtained by adjusting analysis parameters to critical operation conditions. Originality/value – The value of this research work is the integration of the hardware and software necessary for online monitoring of oil condition and mechanical wear. The setup integrates online sampling with data acquisition, wireless communication, change detection and fault recognition computation. The approach has application in non-destructive online condition-based maintenance.

Diagnosis of bearings and gears, traditionally uses the envelope (i.e., demodulation) approach. The spectral kurtosis (SK) is a technique used to identify frequency bands for demodulation. These frequency bands are related to the structural resonances, excited by a series of fault-induced impulses. The novel approach for bearing/gear local fault diagnosis is proposed, based on division of bearing/gear vibration signals into specially defined short duration segments and simultaneous processing of SKs of all these segments for damage diagnosis. The SK-filtered vibrations are used for diagnostic feature extraction further subjected to the decision-making process, based on k-means and k-nearest neighbors. The important feature of the proposed approach is robustness to random slippage in bearings. The experimental validation of a bearing inner race local defects (1.2% relative damage size), and simulated gear vibration (15% relative pitting size), shows a very good diagnostic performance on bearing vibrations and gear vibrations to diagnose local faults. Novel diagnostic effectiveness comparison between the proposed technology and wavelet-based technology is performed for diagnosis of local bearing damage.