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

Digital Pixel Test Structures implemented in a 65 nm CMOS process

The ALICE ITS3 (Inner Tracking System 3) upgrade project and the CERN EP R&D on monolithic pixel sensors are investigating the feasibility of the Tower Partners Semiconductor Co. 65 nm process for use in the next generation of vertex detectors. The ITS3 aims to employ wafer-scale Monolithic Active Pixel Sensors thinned down to 20 to 40 um and bent to form truly cylindrical half barrels. Among the first critical steps towards the realisation of this detector is to validate the sensor technology through extensive characterisation both in the laboratory and with in-beam measurements. The Digital Pixel Test Structure (DPTS) is one of the prototypes produced in the first sensor submission in this technology and has undergone a systematic measurement campaign whose details are presented in this article. The results confirm the goals of detection efficiency and non-ionising and ionising radiation hardness up to the expected levels for ALICE ITS3 and also demonstrate operation at +20 C and a detection efficiency of 99% for a DPTS irradiated with a dose of $10^{15}$ 1 MeV n$_{\mathrm{eq}}/$cm$^2$. Furthermore, spatial, timing and energy resolutions were measured at various settings and irradiation levels.Comment: Updated threshold calibration method. Implemented colorblind friendly color palette in all figures. Updated reference

Helicopter big data processing and predictive analytics: feedback and perspectives

This paper offers a comprehensive return of experience on the deployment of big data technologies enabling various descriptive and predictive analytics within the helicopter industry. It shows how these technologies can efficiently be employed to allow storing and processing the large quantity of flight data which are made available, and consequently how they offer additional analytics capabilities to the analysts. In order to demonstrate these benefits, the paper presents applications concerning statistical fleet data analysis and predictive maintenance. Concluding remarks are then given with particular attention to limitations of distributed big data technologies and challenges regarding their adoption within the industry

Impact of pulse time uncertainty on synchronous average: statistical analysis and relevance to rotating machinery diagnosis

Time synchronous averaging for the extraction of periodic waveforms is a rather common processing method for rotating machinery diagnosis. By synchronizing the signal to the rotational angle of the component of interest, e.g. by using a keyphasor reference signal, it is possible to perform the averaging in the angular domain, thus obtaining an angle-synchronous signal. Jittering of the reference signal affects the quality of the synchronous averaging process, resulting in attenuation and uncertain estimation of the extracted synchronous signal, especially in the high frequency band. In this paper, the effects of random uncertainty in the pulse arrival times of the reference signal on the synchronous averaging method are studied, with the objective of assessing the relevance of such a jitter error to the extracted waveform and the indicators derived for monitoring purposes. First, a unified framework for the computed order tracking method is presented, and then a model linking the statistics of the random jitter to the statistics of the waveform extracted through synchronous averaging in angle domain is developed. The theoretical model connects the random jitter distribution, the number of averaged periods and the ratio of the period of interest to the reference trigger period, to the distribution of the amplitudes of the synchronous frequency components in the synchronously averaged signal. Approximate analytical solutions are derived for cases of interest, allowing the prediction of the attenuation bias and variability of the extracted components amplitudes. The model is first verified against numerical simulations in order to assess consistency, and then parametric studies are presented. Experimental validation is performed on both an experimental and an operational data sets involving respectively a helicopter gearbox and a helicopter fleet

Development of a vibration monitoring strategy based on cyclostationary analysis for the predictive maintenance of helicopter gearbox bearings

The scope of this paper is the development of a fault detection and diagnosis method aimed to helicopter gearbox bearings vibration monitoring in an operational context. Bearings are critical components in the gearbox, and their monitoring allows for failure anticipation capabilities, leading to increased safety and improved maintenance planning. Deploying a monitoring strategy for helicopter gearboxes necessitates the development of a methodology which can provide reliable information under varying operating conditions, dealing with a noisy vibration environment and simultaneously considering acquisition system constraints, such as limited acquisition duration and sampling frequency, and operational needs, such as low rate of false alarms and minimal workload for the analyst. The approach proposed in this paper is based on the cyclostationary signals theory and relies on a two-steps procedure of detection and diagnosis. First, bearing fault detection indicators are devised on a statistical basis, leveraging on the theoretical properties of the envelope method. Then, a diagnosis based on the computation of the averaged cyclic periodogram is performed to assess the damage in the eventuality of an alarm. The developed methodology is validated on real helicopter data collected over about twenty thousand f light hours, including four bearings from different machines for which in-service spalling initiation occurred. The fault detection performance is evaluated on the basis of the achieved false alarm rates and the improvement in fault anticipation with respect to chip detectors, whereas the capability of isolating the fault-related signals using cyclostationary signal separation methods is shown for the diagnosis stage