DBSCAN is a base algorithm for density based clustering. It can detect the clusters of different shapes and sizes from the large amount of data which contains noise and outliers. However, it is fail to handle the local density variation that exists within the cluster. In this paper, we propose a density varied DBSCAN algorithm which is capable to handle local density variation within the cluster. It calculates the growing cluster density mean and then the cluster density variance for any core object, which is supposed to be expended further, by considering density of its -neighborhood with respect to cluster density mean. If cluster density variance for a core object is less than or equal to a threshold value and also satisfying the cluster similarity index, then it will allow the core object for expansion. The experimental results show that the proposed clustering algorithm gives optimized results. General TermsDensity Based Clustering

Purpose -The dependency on human expertise for analysis and interpretation is the main reason for wear debris analysis not being used in industry to its full potential and becoming one of the most powerful machine condition monitoring strategies. The dependency on human expertise makes the interpretation and result subjective in nature, costly and time consuming. The purpose of this paper is to review work being done to develop an automatic, reliable and objective wear particle classification system as a solution to the above problem. At the same time it also aims to discuss some common off line test methods being practiced for wear debris analysis. Design/methodology/approach -Computer image analysis is a solution for some of the problems associated with the conventional techniques. First it is tried to efficiently describe the characteristics of computer images of different types of wear debris using a few numerical parameters. Then using some Artificial Intelligence tools, the wear particle classification system can be developed. Findings -Many shape, size and surface parameters are discussed in the paper. Out of these, nine numerical parameters are selected to describe and distinguish six common type of wear debris. Once the type of debris is identified, the mode of wear and hence the machine condition can be assessed. Practical implications -The present process of fault and condition monitoring of an equipment by wear debris analysis involves human judgment of debris formations. A set-up standard for comparison of debris will enable the maintenance team to diagnose faults in a comparatively better way. Originality/value -The aim of this paper is to discuss the difficulties in identifying wear particles and finding out the exact health of equipment, which, due to its subjective nature, is influenced by human errors. An objective method with certain standards for classification of wear particles compatible with an artificial intelligence system will yield some flawless results of wear debris analysis, which has not been attempted in the past as per available literature.

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Analysis of wear debris, vibration and temperature of journal bearing has been integrated to increase the accuracy in fault diagnosis of a hydropower plant. Samples of used lubricating oil, vibration data and bearing temperature at different intervals were collected. Wear particles and acceleration caused by vibration were analysed for the fault detections. An abnormal increase in the temperature and vibrational energy was observed after 200 days of continuous operations. In the last sample, an abnormal increase in aspect ratio of the wear particles was also observed. Scratches and wiping mark were found over the surface of bearing block and side thrust pad. This confirmed the fault of machine by the analysis of condition monitoring data. Further rectification was done by the replacement of bearing block.

Background: Conventional glass ionomer cement (CGIC) has many beneficial properties, but it has poor physical and mechanical properties. Therefore, new glass ionomer cement (GIC) is manufactured by adding zinc to improve the mechanical properties of GIC ChemFil Rock. This material possesses better flexural tensile strength and compressive strength in comparison to conventional to CGIC. Objectives: The aim of this study was to compare four properties of ZRGI like fracture toughness, surface micro-hardness, abrasive wear, and roughness to other GIC material, which are commercially available as: resin-coated glass ionomer (EQUIA FIL). Materials and Methods: The study was done in dual phase. In phase-1, micro-hardness surface roughness, abrasion of four GIC and a composite resin as control was analyzed and in phase-2, fracture toughness of four GIC was done at 24 h interval so that all cement achieve its peak strength. Results: Micro-hardness value of ChemFil Rock was lowest among different GIC groups. All four GIC group exhibit similar abrasion capacities, while composite were more wear-resistant significantly. Roughness change was highest on ChemFil Rock compared to other GIC. EQUIA FIL has the highest fracture toughness, followed by ChemFil Rock. Conclusion: We can conclude that incorporating zinc in the matrix of chemfil rock increases fracture toughness and good abrasive wear, but it does not improve micro-hardness or surface roughness.