Using independent component analysis scheme for helicopter main gearbox bearing defect identification

© 2017 IEEE. Vibration signal analysis is the most common technique for helicopter health condition monitoring. It has been widely employed to detect helicopter gearbox fault and ensure the safe operation. Through the years, vibration signal analysis has a significant contribution to successfully prevent a number of accidents. However, vibration based bearing identification remains a challenge because bearing defects signatures are contaminated by strong background noise. In this paper, the independent component analysis (ICA) scheme was utilized to analyze vibration signals captured from a CS29 Category 'A' helicopter main gearbox, where bearing faults were seeded on the second epicyclic stage planetary gears bearing. The ICA scheme could separate the multichannel signals into the mutually independent components. The bearing defect signature can be clearly observed in one of the independent components. The analysis result showed that ICA scheme is a promising method for detecting the bearing fault signatures

Information Reconstruction Method for Improved Clustering and Diagnosis of Generic Gearbox Signals

Gearbox is a very complex mechanical system that can generate vibrations from its various elements such as gears, shafts, and bearings. Transmission path effect, signal coupling, and noise contamination can further induce difficulties to the development of a prognostics and health management (PHM) system for a gearbox. This paper introduces a novel information reconstruction approach to clustering and diagnosis of gearbox signals in varying operating conditions. First, vibration signal is transformed from time domain to frequency domain with Fast Fourier Transform (FFT). Then, reconstruction filters are employed to sift the frequency components in FFT spectrum to retain the information of interest. Features are further extracted to calculate the coefficients of the reconstructed energy expression. Then, correlation analysis (CA) and distance measurement (DM) techniques are utilized to cluster signals under diverse shaft speeds and loads. Finally, energy coefficients are used as health indicators for the purpose of fault diagnosis of the rotating elements in the gearbox. The proposed method was used to solve the gearbox problem of the 2009 PHM Conference Data Analysis Competition and won with the best score in both professional and student categories

Rotorcraft Health Management Issues and Challenges

This paper presents an overview of health management issues and challenges that are specific to rotorcraft. Rotorcraft form a unique subset of air vehicles in that their propulsion system is used not only for propulsion, but also serves as the primary source of lift and maneuvering of the vehicle. No other air vehicle relies on the propulsion system to provide these functions through a transmission system with single critical load paths without duplication or redundancy. As such, health management of the power train is a critical and unique part of any rotorcraft health management system. This paper focuses specifically on the issues and challenges related to the dynamic mechanical components in the main power train. This includes the transmission and main rotor mechanisms. This paper will review standard practices used for rotorcraft health management, lessons learned from fielded trials, and future challenges

A model-based reasoning architecture for system-level fault diagnosis

This dissertation presents a model-based reasoning architecture with a two fold purpose: to detect and classify component faults from observable system behavior, and to generate fault propagation models so as to make a more accurate estimation of current operational risks. It incorporates a novel approach to system level diagnostics by addressing the need to reason about low-level inaccessible components from observable high-level system behavior. In the field of complex system maintenance it can be invaluable as an aid to human operators. The first step is the compilation of the database of functional descriptions and associated fault-specific features for each of the system components. The system is then analyzed to extract structural information, which, in addition to the functional database, is used to create the structural and functional models. A fault-symptom matrix is constructed from the functional model and the features database. The fault threshold levels for these symptoms are founded on the nominal baseline data. Based on the fault-symptom matrix and these thresholds, a diagnostic decision tree is formulated in order to intelligently query about the system health. For each faulty candidate, a fault propagation tree is generated from the structural model. Finally, the overall system health status report includes both the faulty components and the associated at risk components, as predicted by the fault propagation model.Ph.D.Committee Chair: Vachtsevanos, George; Committee Member: Liang, Steven; Committee Member: Michaels, Thomas; Committee Member: Vela, Patricio; Committee Member: Wardi, Yora

Fault detection of helicopter gearboxes using the multi-valued influence matrix method

In this paper we investigate the effectiveness of a pattern classifying fault detection system that is designed to cope with the variability of fault signatures inherent in helicopter gearboxes. For detection, the measurements are monitored on-line and flagged upon the detection of abnormalities, so that they can be attributed to a faulty or normal case. As such, the detection system is composed of two components, a quantization matrix to flag the measurements, and a multi-valued influence matrix (MVIM) that represents the behavior of measurements during normal operation and at fault instances. Both the quantization matrix and influence matrix are tuned during a training session so as to minimize the error in detection. To demonstrate the effectiveness of this detection system, it was applied to vibration measurements collected from a helicopter gearbox during normal operation and at various fault instances. The results indicate that the MVIM method provides excellent results when the full range of faults effects on the measurements are included in the training set

The application of time encoded signals to automated machine condition classification using neural networks

This thesis considers the classification of physical states in a simplified gearbox using acoustical data and simple time domain signal shape characterisation techniques allied to a basic feedforward multi-layer perceptron neural network. A novel extension to the signal coding scheme (TES), involving the application of energy based shape descriptors, was developed. This sought specifically to improve the techniques suitability to the identification of mechanical states and was evaluated against the more traditional minima based TES descriptors. The application of learning based identification techniques offers potential advantages over more traditional programmed techniques both in terms of greater noise immunity and in the reduced requirement for highly skilled operators. The practical advantages accrued by using these networks are studied together with some of the problems associated in their use within safety critical monitoring systems.Practical trials were used as a means of developing the TES conversion mechanism and were used to evaluate the requirements of the neural networks being used to classify the data. These assessed the effects upon performance of the acquisition and digital signal processing phases as well as the subsequent training requirements of networks used for accurate condition classification. Both random data selection and more operator intensive performance based selection processes were evaluated for training. Some rudimentary studies were performed on the internal architectural configuration of the neural networks in order to quantify its influence on the classification process, specifically its effect upon fault resolution enhancement.The techniques have proved to be successful in separating several unique physical states without the necessity for complex state definitions to be identified in advance. Both the computational demands and the practical constraints arising from the use of these techniques fall within the bounds of a realisable system

Advanced Rotorcraft Transmission (ART) program

Work performed by the McDonnell Douglas Helicopter Company and Lucas Western, Inc. within the U.S. Army/NASA Advanced Rotorcraft Transmission (ART) Program is summarized. The design of a 5000 horsepower transmission for a next generation advanced attack helicopter is described. Government goals for the program were to define technology and detail design the ART to meet, as a minimum, a weight reduction of 25 percent, an internal noise reduction of 10 dB plus a mean-time-between-removal (MTBR) of 5000 hours compared to a state-of-the-art baseline transmission. The split-torque transmission developed using face gears achieved a 40 percent weight reduction, a 9.6 dB noise reduction and a 5270 hour MTBR in meeting or exceeding the above goals. Aircraft mission performance and cost improvements resulting from installation of the ART would include a 17 to 22 percent improvement in loss-exchange ratio during combat, a 22 percent improvement in mean-time-between-failure, a transmission acquisition cost savings of 23 percent of $165K, per unit, and an average transmission direct operating cost savings of 33 percent, or$24K per flight hour. Face gear tests performed successfully at NASA Lewis are summarized. Also, program results of advanced material tooth scoring tests, single tooth bending tests, Charpy impact energy tests, compact tension fracture toughness tests and tensile strength tests are summarized

Research and technology highlights of the Lewis Research Center

Highlights of research accomplishments of the Lewis Research Center for fiscal year 1984 are presented. The report is divided into four major sections covering aeronautics, space communications, space technology, and materials and structures. Six articles on energy are included in the space technology section

A Machine Learning approach for damage detection and localisation in Wind Turbine Gearbox Bearings

Increasing demand for renewable sources requires more cost-effective solutions to mitigate the cost of maintenance and produce more energy. Preventive maintenance is the most normally adopted scheme in industry for maintenance but despite being well accepted has severe limitations. Its inability to intelligently schedule maintenance at the right time and prevent unexpected breakdowns are the main downsides of this approach and consequently leads to several problems such as unnecessary maintenances. This strategy does not justify the additional costs and thereby represents a negative aspect for renewable energy resource companies that try to generate cost-competitive energy. These challenges are progressively leading towards the predictive maintenance approach to overcome these aforementioned issues. Wind Turbine Gearbox Bearings have received a lot of attention due to the high incidence failure rates provoked by the harsh operational and environmental conditions. Current techniques only reach a level one of diagnostics commonly known as the Novelty Detection stage and normally requires the expertise of a skilled operator to interpret data and infer damage from it. A data-driven approach by using Machine Learning methods has been used to tackle the damage detection and location stage in bearing components. The damage location was performed by using non-destructive methods such as the Acoustic Emission technique — these measurements were used as features to locate damage around the bearing component once the damage was detected. The implementation of this stages also led to the exploration of damage generation due to overload defects and proposed a methodology to simulate these defects in bearings — the study of this concept was implemented in a scaled-down experiment where damage detection and localisation was performed. Due to the importance of the implementation of a damage location stage, damage in AE sensors was also explored in this work. Features extracted from impedance curves allowed to train Machine Learning methods to trigger a novelty when a bonding scenario occurred. This ultimately allowed the identification of unhealthy sensors in the network that could potentially generate spurious results in the damage predictions stage