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 data-driven approach to fault diagnostics for industrial process plants based on feature extraction and inferential statistics

Accurate detection and diagnostics of faults in complex industrial plants are important for preventing unplanned downtime, optimizing operations and maintenance decisions, minimizing repair time, and optimizing spare part logistics. It is often infeasible to generate accurate physics-based models of complex equipment; therefore, and due to lower computational complexity, data-driven methods are frequently employed. We propose a novel method for data-driven fault diagnostics and validate it using the Tennessee Eastman process (TEP) benchmark. It is assumed that the time of the onset of the fault is known, such that time-series data from the process both before and after occurrence of the fault can be extracted. For each of the measured time-series, several statistical features are extracted. A statistical significance level is computed for each feature using inferential statistics measures. The matrix of significance levels serves as a ``fingerprint'' of each fault category and is used as input to a feedforward neural network. We show that the network can be trained to achieve high classification accuracy on data from the TEP benchmark model

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

Helicopter Vibration Health Monitoring Systems Featuring Engine Vibration Monitoring

Airbus Helicopter provides Health and Usage Monitoring Systems (HUMS) offering a broad range of functions like on-board Rotor Tuning, Vibration Health Monitoring and Engine Vibration Monitoring (EVM) as feature of vibration monitoring. For the latter, Airbus Helicopters cooperates with SAFRAN Helicopter Engines in the frame of data collection, data processing and data transfer to assure the maximum of benefit for the customer. A clear objective is to provide HUM system compliant with operational requirements and guidelines from UK CAA, CAA Norway, industry associations like SAE and working groups like IHST. Classical vibration based health algorithm require information from analogical speed sensors for signal processing methods like time synchronous averaging and feature extraction. Airbus Helicopters proposes an approach without analogical speed interface, but extracting necessary information from the vibration and digital speed signals. This technique makes use of the speed interface which is part of the baseline helicopter and thus omits any electrical modification of the parts. This paper presents the approach used by Airbus Helicopter in cooperation with SAFRAN Helicopter Engines in the area of feature extraction and condition indicator computation using vibration signals and digital information from the helicopter avionic system HELIONIX