Fault detection in operating helicopter drive train components based on support vector data description

The objective of the paper is to develop a vibration-based automated procedure dealing with early detection of mechanical degradation of helicopter drive train components using Health and Usage Monitoring Systems (HUMS) data. An anomaly-detection method devoted to the quantification of the degree of deviation of the mechanical state of a component from its nominal condition is developed. This method is based on an Anomaly Score (AS) formed by a combination of a set of statistical features correlated with specific damages, also known as Condition Indicators (CI), thus the operational variability is implicitly included in the model through the CI correlation. The problem of fault detection is then recast as a one-class classification problem in the space spanned by a set of CI, with the aim of a global differentiation between normal and anomalous observations, respectively related to healthy and supposedly faulty components. In this paper, a procedure based on an efficient one-class classification method that does not require any assumption on the data distribution, is used. The core of such an approach is the Support Vector Data Description (SVDD), that allows an efficient data description without the need of a significant amount of statistical data. Several analyses have been carried out in order to validate the proposed procedure, using flight vibration data collected from a H135, formerly known as EC135, servicing helicopter, for which micro-pitting damage on a gear was detected by HUMS and assessed through visual inspection. The capability of the proposed approach of providing better trade-off between false alarm rates and missed detection rates with respect to individual CI and to the AS obtained assuming jointly-Gaussian-distributed CI has been also analysed

A pre-posterior analysis framework for quantifying the value of seismic monitoring and inspections of buildings

Peer reviewe

Chaos in computer performance

Modern computer microprocessors are composed of hundreds of millions of transistors that interact through intricate protocols. Their performance during program execution may be highly variable and present aperiodic oscillations. In this paper, we apply current nonlinear time series analysis techniques to the performances of modern microprocessors during the execution of prototypical programs. Our results present pieces of evidence strongly supporting that the high variability of the performance dynamics during the execution of several programs display low-dimensional deterministic chaos, with sensitivity to initial conditions comparable to textbook models. Taken together, these results show that the instantaneous performances of modern microprocessors constitute a complex (or at least complicated) system and would benefit from analysis with modern tools of nonlinear and complexity science

Debt concentration and secondary market prices

Using a model that distinguishes between large money center banks and smaller regional banks, this paper shows that the percentage of a country's debt held by the large banks affects the secondary market price of that country's debt: the higher the concentration of the debt, the higher the secondary market price. It also shows that if debt is freely traded in the secondary market, the entire stock of debt will not eventually end up being owned by the large banks. The authors'empirical analysis incorporates several potential determinants of secondary market prices: variables associated with a country's economic performance, variables that can be associated with the creditor country's regulatory structure, and the concentration of debt in the hands of the largest U.S. banks.Banks&Banking Reform,Financial Intermediation,Economic Theory&Research,Financial Crisis Management&Restructuring,Environmental Economics&Policies

Business cycles and leading indicators of industrial activity in India

The identification of business cycles in India and construction of a composite leading indicator for forecasting the cyclical turning points have been the focus of this study. The cyclical analysis of monthly index of industrial production (IIP) in India applying the Bry-Boschan procedure indicates that there have been 13 growth cycles in the Indian economy with varying durations during 1970-71 to 2001-02. While the average duration of expansion has been 12 months, the recessions are characterised by relatively longer duration of 16 months. For the purpose of forecasting turning points of business cycle, a composite leading index (CLI) is constructed comprising non-oil imports, exports, US GDP, deposits of commercial banks, non-food credit of commercial banks, currency demand, money supply growth, prices of industrial raw materials, prices of manufactured products, treasury bill yield, stock prices, freight loading of the railways and cargo handled at the major ports. The CLI has been able to forecast the turning points of the reference series with a lead period of about 6 months.business cycles; leading indicators

Control charts for the on-line diagnostics of CMM performance

The quality of a production process is increasing its dependence on both the manufacturing technology, and the production control. In most applications controls are operated by relying on intelligent instrumentation to 'automatically' perform the programmed checks. However, the performance systems that verify the product's quality can deteriorate, as can the production process. This paper presents a method for the on-line verification of the performance of a coordinate measuring machine (CMM) using statistically based control charts. The method is automated and performed on-line during a normal measurement cycle. Some experimental results are then presented and discussed

The Use of Qualitative Business TendencySurveys for Forecasting Business Investmentin Germany

Investment in equipment and machinery is a very important component of GDP. In this paper we examine whether data from business tendency surveys are useful for a timely assessment of current investment behavior. In addition we investigate whether the survey results are helpful for forecastinginvestment growth in the short run. The first question is addressed with thehelp of spectral analysis. To study the forecast ability we estimate linearautoregressive and additive autoregressive models. The forecasting performance is assessed through filtered residuals. The analyses show that the business survey is indeed a useful tool for assessing investment in equipment and machinery.Business tendency surveys, forecasting, investment, linear autoregression, additive autoregression