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The paper presents the comparative study on the antiwear properties of zinc oxide and Cu-Zn mixed oxide nanoparticles, as additive in SAE20W40 engine oil. The nanoparticles were prepared using precipitation method and characterized using scanning microscopy imaging technique and XRD analysis. The particle size was found to be between 70-80 nm.The stability of the nanosuspension play a vital role in the antiwear performance. Therefore the stability studies were carried out by dispersing varying concentration of nanoparticle between 0.01wt% - 0.05wt% in the engine oil using surface modifiers and sonication. Nanosuspensions above 0.02 wt% of nanoparticles showed sedimentation on long standing for 36h. Based on this, the concentration of nanoparticle in the engine oil was optimized as 0.01 and 0.02wt%.The nanosuspensions with optimized concentration i.e.0.01wt% ZnO, 0.02 wt% ZnO, 0.01wt% Cu-Zn and 0.02 wt% Cu- Zn mixed oxides were tested for antiwear property using four ball tester.

Even though hydraulic brakes are valuable safety elements for riders, they are necessary for braking in a good condition. Vibration signatures may be used to assess the condition of the brake components. In this research, the monitor and make status tracking and the dynamic data acquisition method with a piezo-electric transducer is suggested as a promising approach to these challenges by machine-learning. The Ford EcoSport rig was used to get the vibration signal for good and bad braking conditions. The vibration signals had analytical mathematical predictive characteristics, and the decision tree model, J48 was used in the selection of the signals. In order to define a certain concern, a structural decision does not state the number of features required. Therefore, to find the right number of features a rigorous analysis is required.

Car hydraulic brakes are important safety components for passengers and are thus the good condition of brakes are essential for braking. By using the vibrational signatures, the state of the brake components can be determined. In this proposed study, electronic condition monitoring is suggested as a possible solution to such issues by using a machine learning method with a piezo-electric transducer and a dynamic data acquisition system. Ford EcoSport setup was used to acquire the vibration signals for both good and bad braking conditions. The mathematical Descriptive statistical features from the vibration signals were obtained and the feature selection has been done with the C4.5 decision tree classifier. The appropriate number of features needed to classify a particular problem is not determined by a specific method. A thorough study is, therefore, necessary to find the right number of features.