Statistical gear health analysis which is robust to fluctuating loads and operating speeds

Condition-based maintenance is concerned with the collection and interpretation of data to support maintenance decisions. The non-intrusive nature of vibration data enables the monitoring of enclosed systems such as gearboxes. It remains a significant challenge to analyze vibration data that are generated under fluctuating operating conditions. This is especially true for situations where relatively little prior knowledge regarding the specific gearbox is available. It is therefore investigated how an adaptive time series model, which is based on Bayesian model selection, may be used to remove the non-fault related components in the structural response of a gear assembly to obtain a residual signal which is robust to fluctuating operating conditions. A statistical framework is subsequently proposed which may be used to interpret the structure of the residual signal in order to facilitate an intuitive understanding of the condition of the gear system. The proposed methodology is investigated on both simulated and experimental data from a single stage gearbox. © 2011 Elsevier Ltd. All rights reserved.

Systematic misregistration and the statistical analysis of surface data.

Spatial normalisation is a key element of statistical parametric mapping and related techniques for analysing cohort statistics on voxel arrays and surfaces. The normalisation process involves aligning each individual specimen to a template using some sort of registration algorithm. Any misregistration will result in data being mapped onto the template at the wrong location. At best, this will introduce spatial imprecision into the subsequent statistical analysis. At worst, when the misregistration varies systematically with a covariate of interest, it may lead to false statistical inference. Since misregistration generally depends on the specimen's shape, we investigate here the effect of allowing for shape as a confound in the statistical analysis, with shape represented by the dominant modes of variation observed in the cohort. In a series of experiments on synthetic surface data, we demonstrate how allowing for shape can reveal true effects that were previously masked by systematic misregistration, and also guard against misinterpreting systematic misregistration as a true effect. We introduce some heuristics for disentangling misregistration effects from true effects, and demonstrate the approach's practical utility in a case study of the cortical bone distribution in 268 human femurs.

Independent component analysis of microarray data in the study of endometrial cancer.

Gene microarray technology is highly effective in screening for differential gene expression and has hence become a popular tool in the molecular investigation of cancer. When applied to tumours, molecular characteristics may be correlated with clinical features such as response to chemotherapy. Exploitation of the huge amount of data generated by microarrays is difficult, however, and constitutes a major challenge in the advancement of this methodology. Independent component analysis (ICA), a modern statistical method, allows us to better understand data in such complex and noisy measurement environments. The technique has the potential to significantly increase the quality of the resulting data and improve the biological validity of subsequent analysis. We performed microarray experiments on 31 postmenopausal endometrial biopsies, comprising 11 benign and 20 malignant samples. We compared ICA to the established methods of principal component analysis (PCA), Cyber-T, and SAM. We show that ICA generated patterns that clearly characterized the malignant samples studied, in contrast to PCA. Moreover, ICA improved the biological validity of the genes identified as differentially expressed in endometrial carcinoma, compared to those found by Cyber-T and SAM. In particular, several genes involved in lipid metabolism that are differentially expressed in endometrial carcinoma were only found using this method. This report highlights the potential of ICA in the analysis of microarray data.

Assessing seasonal precipitation trends in India using parametric and non-parametric statistical techniques

This paper provides an insight into the long-term trends of the four seasonal and annual precipitations in various climatological regions and sub-regions in India. The trends were useful to investigate whether Indian seasonal rainfall is changing in terms of magnitude or location-wise. Trends were assessed over the period of 1954-2003 using parametric ordinary least square fits and non-parametric Mann-Kendall technique. The trend significance was tested at the 95% confidence level. Apart from the trends for individual climatological regions in India and the average for the whole of India, trends were also specifically determined for the possible smaller geographical areas in order to understand how different the trends would be from the bigger spatial scales. The smaller geographical regions consist of the whole southwestern continental state of Kerala. It was shown that there are decreasing trends in the spring and monsoon rainfall and increasing trends in the autumn and winter rainfalls. These changes are not always homogeneous over various regions, even in the very short scales implying a careful regional analysis would be necessary for drawing conclusions regarding agro-ecological or other local projects requiring change in rainfall information. Furthermore, the differences between the trend magnitudes and directions from the two different methods are significantly small and fall well within the significance limit for all the cases investigated in Indian regions (except where noted). © 2010 Springer-Verlag.