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The purpose of this paper is to develop new knowledge in experimental characterization of contaminants in engine lubricants, using surface plasmon resonance (SPR) sensing that can be applicable for on‐line condition monitoring of lubricant quality and engine component performance.

The effect of change in optical properties (e.g. transparency, absorption, and refractive index) of engine lubricants caused by the introduction of contaminants, such as gasoline, coolant, and water, on the surface plasmon resonance characteristics is analyzed experimentally. In SPR measurement, variations in both the refractive index and absorption cause changes in the SPR curve, which is the dependence of reflectivity vs incidence angle. The SPR characteristics (e.g. refractivity) of engine lubricant contaminated by gasoline, water and coolant at different concentration are measured as a function of resonance angle and analyzed with respect to different concentration (1%‐10%) of contaminants. Also, pattern recognition analysis between fresh and used engine lubricants is performed, to show applicability of Bayesian classification methodology for on‐line monitoring and predicting engine lubricant condition.

It was shown experimentally that attenuation of surface plasmons due to introduction of contaminants to the engine lubricant leads to a noticeable change in resonance angle and reflectivity minimum of the SPR curve due to an increase in the dielectric permittivity. In addition, the changes in the SPR characteristics were observed between fresh and used engine lubricant, causing resonance angle and reflectivity minimum of the SPR curve to shift.

The knowledge generated in this study lays the informational basis to further develop an on‐line system for engine lubricant condition monitoring using miniaturized SPR sensors fully suitable for on board applications.

SPR characterization is originally applied for analysis of optical properties of engine lubricants caused by the introduction of contaminants, such as gasoline, coolant, and water.

The purpose of this paper is to develop a maintenance decision support system (DSS) framework using in-service lubricant data for fault diagnosis. The DSS reveals embedded patterns in the data (knowledge discovery) and automatically quantifies the influence of lubricant parameters on the unhealthy state of the machine using alternative classifiers. The classifiers are compared for robustness from which decision-makers select an appropriate classifier given a specific lubricant data set.

The DSS embeds a framework integrating cluster and principal component analysis, for feature extraction, and eight classifiers among them extreme gradient boosting (XGB), random forest (RF), decision trees (DT) and logistic regression (LR). A qualitative and quantitative criterion is developed in conjunction with practitioners for comparing the classifier models.

The results show the importance of embedded knowledge, explored via a knowledge discovery approach. Moreover, the efficacy of the embedded knowledge on maintenance DSS is emphasized. Importantly, the proposed framework is demonstrated as plausible for decision support due to its high accuracy and consideration of practitioners needs.

The proposed framework will potentially assist maintenance managers in accurately exploiting lubricant data for maintenance DSS, while offering insights with reduced time and errors.

Advances in lubricant-based intelligent approach for fault diagnosis is seldom utilized in practice, however, may be incorporated in the information management systems offering high predictive accuracy. The classification models' comparison approach, will inevitably assist the industry in selecting amongst divergent models' for DSS.

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.

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.

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.

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.

Engine oil degrades in quality during its use and after certain period of time the oil needs to be changed depending upon its condition. The purpose of this paper is to design and develop an online condition monitoring device for engine oil.

Based on the previous works in this line and some testing of used oils in the laboratory, the correlation of change in colour with other properties were identified. An optical colour sensor was then designed and developed which can transform the darkness of oil colour into electrical resistance. A series of tests were undertaken to calibrate the system for its correctness.

This type of sensor provides the information about the condition of the oil and also can inform about the probable time for drain‐off of the oil.

Engine oil changes are normally done by schedules which are highly conservative and cost the user as the oil is changed when it could be still used for some time. Use of an online sensor will minimize the cost on lubricants to some extent.

The device is of great value to the users of IC engines as it not only reduces the cost on lubricants but also informs the user about the present condition of the oil.

This paper describes the development of the use of lubricant analytical programmes and trend analysis to optimise oil change intervals and to predict equipment failure. The various analytical methods are covered, as are the most frequently occurring lubricant applications where such condition monitoring programmes are most appropriate.

Most catastrophic failures of machines result from improper lubrication. The cost of lubricants can be up to 10‐12 percent of the production cost an essential part of maintenance management, therefore it is essential that the optimization of lubricants is at the optimum. This paper decribes and considers present practice in lubricant condition monitoring.

The involvement of wear, friction and lubrication in engineering systems and industrial applications makes it imperative to study the various aspects of tribology in relation with advanced technologies and concepts. The concept of Industry 4.0 and its implementation further faces a lot of barriers, particularly in developing economies. Real-time and reliable data is an important enabler for the implementation of the concept of Industry 4.0. For availability of reliable and real-time data about various tribological systems is crucial in applying the various concepts of Industry 4.0. This paper aims to attempt to highlight the role of sensors related to friction, wear and lubrication in implementing Industry 4.0 in various tribology-related industries and equipment.

A through literature review has been done to study the interrelationships between the availability of tribology-related data and implementation of Industry 4.0 are also discussed. Relevant and recent research papers from prominent databases have been included. A detailed overview about the various types of sensors used in generating tribological data is also presented. Some studies related to the application of machine learning and artificial intelligence (AI) are also included in the paper. A discussion on fault diagnosis and cyber physical systems in connection with tribology has also been included.

Industry 4.0 and tribology are interconnected through various means and the various pillars of Industry 4.0 such as big data, AI can effectively be implemented in various tribological systems. Data is an important parameter in the effective application of concepts of Industry 4.0 in the tribological environment. Sensors have a vital role to play in the implementation of Industry 4.0 in tribological systems. Determining the machine health, carrying out maintenance in off-shore and remote mechanical systems is possible by applying online-real-time data acquisition.

The paper tries to relate the pillars of Industry 4.0 with various aspects of tribology. The paper is a first of its kind wherein the interdisciplinary field of tribology has been linked with Industry 4.0. The paper also highlights the role of sensors in generating tribological data related to the critical parameters, such as wear rate, coefficient of friction, surface roughness which is critical in implementing the various pillars of Industry 4.0.

The first of the one‐day seminars, sponsored by Fluid Power magazine in conjunction with Kuwait Petroleum Lubricants and UCC International, was held near Huddersfield on 6 October.

The purpose of this paper is to provide information on lubricant contamination by biodiesel using vibration and neural network.

The possible contamination of lubricants is verified by analyzing the vibration and neural network of a bench test under determinated conditions.

Results have shown that classical signal analysis methods could not reveal any correlation between the signal and the presence of contamination, or contamination grade. On other hand, the use of probabilistic neural network (PNN) was very successful in the identification and classification of contamination and its grade.

This study was done for some specific kinds of biodiesel. Other types of biodiesel could be analyzed.

Contamination information is presented in the vibration signal, even if it is not evident by classical vibration analysis. In addition, the use of PNN gives a relatively simple and easy‐to‐use detection tool with good confidence. The training process is fast, and allows implementation of an adaptive training algorithm.

This research could be extended to an internal combustion engine in order to verify a possible contamination by biodiesel.

This paper aims to provide an effective method so that the ultrasonic technique can be applied to the online debris particle detection. It proposes utilizing the waveshape features in discriminating the debris particle in lubricant.

The finite element model has been developed to investigate the scattering of the ultrasonic waves in lubricant containing single scatterer, such as the debris particle and the air bubble. The simulation results show that the results verify that different scatterers differ in the waveshape features. The static experiments were carried out on a specially fixture. The single spherical debris, long debris and air bubble were measured. The fast Fourier transform (FFT) method was applied to the analysis of the echo signals to obtain the features implicated in the waveshape.

The research of this paper verifies that different scatterers differ both in their shape features and in the FFT analysis features.

The rapid movement of the debris particles as well as the lubricant temperature may influence the measuring signals. Besides, the measuring signals are usually corrupted by noise, especially for the tiny debris. Therefore, researchers are encouraged to solve those problems further.

The paper includes implications for the improvement in the online debris detection and the development of the ultrasonic technique applied in online debris detection.

The paper provides a promising way that the ultrasonic waveshape features can be utilized to the identify debris particle online.