Objective: Bearing defects are the most frequently occurring fault in any electrical machine. In this perspective, this manuscript proposed a novel statistical time-domain approach utilizing the vibration signal to detect incipient faults of rolling-element bearing used in three-phase induction motor. 
Methodology: To detect bearing defects, six time-domain features (TDFs) namely Mean Value (µ), Peak, Root Mean Square (RMS), Crest Factor (CRF), Skewness (SKW) and Kurtosis (K) were extracted from the standard database of the vibration signal. The standard databases of vibration signals were taken from the publicly available datacenter website of Case Western Reserve University (CWRU) relating to healthy, inner raceway and ball defects of bearing. Initially, the mean and standard deviation analysis of each considered TDFs of vibration signals were performed to discriminate the health conditions of bearing. Then, the box or whisker plot method was applied to visualize the variation in each TDF in terms of median and interquartile range (IQR) value for better analysis of bearing defects. Finally, a new index parameter termed as bearing fault index (BFIT) was also computed and this parameter predicts the bearing defects based on the mean of all considered TDFs.
Results: The results of the “mean±σ” analysis have depicted that all considered TDFs except µ feature are almost independent to operating loads, and have discerning potential to diagnose bearing defects. The computations of these TDFs are mathematically very simple. The box plot representation of TDFs of vibration databases have shown that peak, RMS, and skewness features outperforms to demarcate bearing health conditions in terms of median and IQR value. The results of quantitative analysis of BFIT parameter have shown that if the magnitude of this parameter is higher than 1.8 then bearing is supposed to be faulty at all operating loads of machine. Thus, the BFIT analysis of TDFs is more simple and reliable to discriminate the health conditions of bearing. As most of the available techniques rely on the multi-processing of vibration data that requires large processing time and complicated mathematical model, so the proposed method prove to be simple and reliable in identifying the incipient bearing defects. 

Objective: This paper presents an automated approach that combines Fisher ranking and dimensional reduction method as kernel principal component analysis (KPCA) with support vector machine (SVM) to accurately classify the defects of rolling element bearing used in induction motor.
Methodology: In this perspective, vibration signal produced by rolling element bearing was decomposed to four levels using wavelet packet decomposition (WPD) method. Thirty one Logarithmic Root Mean Square Features (LRMSF) were extracted from four level decomposed vibration signals. Initially, thirty one features were rank by Fisher score and top ten rank features were selected. For effective detection, top ten features were reduced to a new feature using dimension reduction methods as KPCA and generalized discriminant analysis (GDA). After this, the new feature applied to SVM for binary classification of bearing defects. For analysis of this thirty six standard vibration datasets taken from online available bearing data center website of Case Western Reserve University on bearing conditions like healthy (NF), inner race defect (IR) and ball bearing (BB) defects at different loads. 
Results: The simulated numerical results show that proposed method KPCA with SVM classifier using Gaussian Kernel achieved an accuracy (AC) of 100, Sensitivity (SE) of 100%, Specificity (SP) of 99.3% and Positive prediction value (PPV) of 99.3% for NF_IRB dataset, and an AC of 100, SE of 99.8%, SP of 100% and PPV of 100% for NF_BBB dataset.

Recent year’s development in communication technologies have been able to deploy a whole new range of ad hoc networks of moving vehicles namely Vehicular Ad hoc Network (VANET). The key component of Intelligent Transportation Systems (ITS) is VANET only. The Vehicular communication systems is one of the critical complex infrastructure system of any nation. Thus, reliability i.e. having a low failure probability of such critical systems is the main concern of the academia and the industry. This paper primarily focuses on the reliability modelling of VANET. The main objective of the research is to address the issue of quantifying the importance of components in contributing to the reliability and maintenance of a VANET. Reliability Block diagrams (RBD)s have been modelled for the architecture of VANET. Out of various Component Importance Measures (CIM)s available in literature, Birnbaum measure, Improvement measure and Criticality Importance measures have been used to prioritize the system components. The research work is successful in identifying the most critical and the least critical component of the Vehicular Ad Hoc Network and thus provides a solution for the design improvement, maintenance and failure diagnosis.

The present day systems are increasing in complexity in terms of both the size and functionality. Also society demands these systems to be ultra-reliable. Reliability evaluation and optimization techniques play a major role in these regards. However reliability evaluation & optimization techniques do not give any idea about maintenance, risk involved and related cost incurred and criticality of system components or subsystems. Important measures (IM) exist in literature that identify the weak components i.e critical components and give ranking to them. Recently some work has appeared on Cost Importance Measure (CIM). There are number of mistakes/short comings in the paper Cost-related importance measure by Ming et.al. Definition of CIM given in general and the same used for computation of CIM of component xi have appeared differently (Different definitions for CIM). PD(xi),Partial derivative of component xi obtained for most of the components are either inexact or are faulty in expression and computations are wrong. All other mistakes also have not only been pointed but have been corrected also. A New CIM (NCIM) proposed, which highlights the above issues and have done desired calculations. The new CIM which has been advanced is computationally simpler and yields the desired ranking of components.