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Purpose The purpose of this paper is to adapt teachable machine as a web-based tool for recognition of wear pattern and type of wear by training a convolutional neural network (CNN) model. This helps to monitor the health of the lubricated system as a part of condition monitoring. Design/methodology/approach Ferrography technique is used for analysis of wear particles. It helps monitor the condition of lubricated mechanical system. In present paper, CNN model is developed for identifying the type of wear particles coming out of Gearbox system using teachable machine. Findings From the experimentation, it has been observed that the wear severity index has been increased due to increase in wear particle concentration. CNN model has achieved an accuracy of 95.4% to recognize five categories of wear particles. Originality/value Teachable machine is generally used for the prediction of images, gestures and sound features. An attempt is made to apply this model for micro and nano wear particles to classify them based on their characteristics.

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In this paper authors have discussed risk quantification methods and evaluation of risks and decision parameter to be used for deciding on ranking of the critical items, for prioritization of condition monitoring based risk and reliability centered maintenance (CBRRCM). As time passes any equipment or any product degrades into lower effectiveness and the rate of failure or malfunctioning increases, thereby lowering the reliability. Thus with the passage of time or a number of active tests or periods of work, the reliability of the product or the system, may fall down to a low value known as a threshold value, below which the reliability should not be allowed to dip. Hence, it is necessary to fix up the normal basis for determining the appropriate points in the product life cycle where predictive preventive maintenance may be applied in the programme so that the reliability (the probability of successful functioning) can be enhanced, preferably to its original value, by reducing the failure rate and increasing the mean time between failure. It is very important for defence application where reliability is a prime work. An attempt is made to develop mathematical model for risk assessment and ranking them. Based on likeliness coefficient 1 β and risk coefficient 2 β ranking of the sub-systems can be modelled and used for CBRRCM.