A Novel Hybrid Technique Combining Improved Cepstrum Pre-Whitening and High-Pass Filtering for Effective Bearing Fault Diagnosis Using Vibration Data.

Rolling element bearings (REBs) are an essential part of rotating machinery. A localised defect in a REB typically results in periodic impulses in vibration signals at bearing characteristic frequencies (BCFs), and these are widely used for bearing fault detection and diagnosis. One of the most powerful methods for BCF detection in noisy signals is envelope analysis. However, the selection of an effective band-pass filtering region presents significant challenges in moving towards automated bearing fault diagnosis due to the variable nature of the resonant frequencies present in bearing systems and rotating machinery. Cepstrum Pre-Whitening (CPW) is a technique that can effectively eliminate discrete frequency components in the signal whilst detecting the impulsive features related to the bearing defect(s). Nevertheless, CPW is ineffective for detecting incipient bearing defects with weak signatures. In this study, a novel hybrid method based on an improved CPW (ICPW) and high-pass filtering (ICPW-HPF) is developed that shows improved detection of BCFs under a wide range of conditions when compared with existing BCF detection methods, such as Fast Kurtogram (FK). Combined with machine learning techniques, this novel hybrid method provides the capability for automated bearing defect detection and diagnosis without the need for manual selection of the resonant frequencies. The results from this novel hybrid method are compared with a number of established BCF detection methods, including Fast Kurtogram (FK), on vibration signals collected from the project I2BS (An EU Clean Sky 2 project 'Integrated Intelligent Bearing Systems' collaboration between Schaeffler Technologies and the University of Southampton. Safran Aero Engines was the topic manager for this project) and those from three databases available in the public domain-Case Western Reserve University (CWRU), Intelligent Maintenance Systems (IMS) datasets, and Safran jet engine data-all of which have been widely used in studies of this kind. By calculating the Signal-to-Noise Ratio (SNR) of each case, the new method is shown to be effective for a much lower SNR (with an average of 30.21) compared with that achieved using the FK method (average of 14.4) and thus is much more effective in detecting incipient bearing faults. The results also show that it is effective in detecting a combination of several bearing faults that occur simultaneously under a wide range of bearing configurations and test conditions and without the requirement of further human intervention such as extra screening or manual selection of filters.

Exploring the bidirectional causality between green markets and economic policy: evidence from the time-varying Granger test.

The vigorous development of green markets and the effective mitigation of economic policy fluctuations are current hotspots that intrigue our interest in exploring the causal relationships between green market returns and economic policy uncertainty (EPU). Green bonds, corporate environmental responsibility, green technology investment, and the carbon trading market are our research objects to comprehensively understand the interaction among them, from both macro and micro perspectives. Considering the importance of temporal heterogeneity and spillover direction in causation, we employ the time-varying Granger causality method to obtain bidirectional real-time identification. We find that green market returns exhibit a time-varying bidirectional causality with EPU over most of the sample period. In contrast, green markets are more a risk spillover than a recipient. Notably, this causality is vulnerable to exogenous financial risks, especially structural changes caused by the COVID-19 pandemic. Overall, this paper provides insights into the deep-seated causes of price fluctuations, volatile market uncertainty, and the interaction mechanism between them, as well as implications for market participants and policymakers.

Analysis of schizophrenia data using a nonlinear threshold index logistic model.

Genetic information, such as single nucleotide polymorphism (SNP) data, has been widely recognized as useful in prediction of disease risk. However, how to model the genetic data that is often categorical in disease class prediction is complex and challenging. In this paper, we propose a novel class of nonlinear threshold index logistic models to deal with the complex, nonlinear effects of categorical/discrete SNP covariates for Schizophrenia class prediction. A maximum likelihood methodology is suggested to estimate the unknown parameters in the models. Simulation studies demonstrate that the proposed methodology works viably well for moderate-size samples. The suggested approach is therefore applied to the analysis of the Schizophrenia classification by using a real set of SNP data from Western Australian Family Study of Schizophrenia (WAFSS). Our empirical findings provide evidence that the proposed nonlinear models well outperform the widely used linear and tree based logistic regression models in class prediction of schizophrenia risk with SNP data in terms of both Types I/II error rates and ROC curves.

Minimum Hellinger distance estimation for finite mixtures of Poisson regression models and its applications.

Minimum Hellinger distance estimation (MHDE) has been shown to discount anomalous data points in a smooth manner with first-order efficiency for a correctly specified model. An estimation approach is proposed for finite mixtures of Poisson regression models based on MHDE. Evidence from Monte Carlo simulations suggests that MHDE is a viable alternative to the maximum likelihood estimator when the mixture components are not well separated or the model parameters are near zero. Biometrical applications also illustrate the practical usefulness of the MHDE method.