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

Diagnostic Analyzer for Gearboxes (DAG): User's Guide

This documentation describes the Diagnostic Analyzer for Gearboxes (DAG) software for performing fault diagnosis of gearboxes. First, the user would construct a graphical representation of the gearbox using the gear, bearing, shaft, and sensor tools contained in the DAG software. Next, a set of vibration features obtained by processing the vibration signals recorded from the gearbox using a signal analyzer is required. Given this information, the DAG software uses an unsupervised neural network referred to as the Fault Detection Network (FDN) to identify the occurrence of faults, and a pattern classifier called Single Category-Based Classifier (SCBC) for abnormality scaling of individual vibration features. The abnormality-scaled vibration features are then used as inputs to a Structure-Based Connectionist Network (SBCN) for identifying faults in gearbox subsystems and components. The weights of the SBCN represent its diagnostic knowledge and are derived from the structure of the gearbox graphically presented in DAG. The outputs of SBCN are fault possibility values between 0 and 1 for individual subsystems and components in the gearbox with a 1 representing a definite fault and a 0 representing normality. This manual describes the steps involved in creating the diagnostic gearbox model, along with the options and analysis tools of the DAG software

An adaptive observer for on-line tool wear estimation in turning, Part II: Results

The basic concept and design of an adaptive observer for tool wear estimation in turning, based on force measurement, has been presented in the previous paper (Part I). This paper shows that numerical problems in the estimation of the states of tool wear precludes the use of this method in multi-wear cases where both flank wear and crater wear are present. The method can be applied, however, when one type of wear (either flank wear or crater wear) dominates. The method is applied in turning experiments to a case where flank wear is dominant, and to a second case where crater wear dominates. For the first case the flank wear estimates show excellent agreement with actual wear measurements. For the second case the crater wear estimates are satisfactory, but not as good as in the first case.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/26750/1/0000302.pd

Unsupervised Pattern Classifier for Abnormality-Scaling of Vibration Features for Helicopter Gearbox Fault Diagnosis

A new unsupervised pattern classifier is introduced for on-line detection of abnormality in features of vibration that are used for fault diagnosis of helicopter gearboxes. This classifier compares vibration features with their respective normal values and assigns them a value in (0, 1) to reflect their degree of abnormality. Therefore, the salient feature of this classifier is that it does not require feature values associated with faulty cases to identify abnormality. In order to cope with noise and changes in the operating conditions, an adaptation algorithm is incorporated that continually updates the normal values of the features. The proposed classifier is tested using experimental vibration features obtained from an OH-58A main rotor gearbox. The overall performance of this classifier is then evaluated by integrating the abnormality-scaled features for detection of faults. The fault detection results indicate that the performance of this classifier is comparable to the leading unsupervised neural networks: Kohonen's Feature Mapping and Adaptive Resonance Theory (AR72). This is significant considering that the independence of this classifier from fault-related features makes it uniquely suited to abnormality-scaling of vibration features for fault diagnosis

Diagnosis of helicopter gearboxes using structure-based networks

A connectionist network is introduced for fault diagnosis of helicopter gearboxes that incorporates knowledge of the gearbox structure and characteristics of the vibration features as its fuzzy weights. Diagnosis is performed by propagating the abnormal features of vibration measurements through this Structure-Based Connectionist Network (SBCN), the outputs of which represent the fault possibility values for individual components of the gearbox. The performance of this network is evaluated by applying it to experimental vibration data from an OH-58A helicopter gearbox. The diagnostic results indicate that the network performance is comparable to those obtained from supervised pattern classification

An adaptive observer for on-line tool wear estimation in turning, Part I: Theory

On-line sensing of tool wear has been a long-standing goal of the manufacturing engineering community. In the absence of any reliable on-line tool wear sensors, a new model-based approach for tool wear estimation has been proposed. This approach is an adaptive observer, based on force measurement, which uses both parameter and state estimation techniques. The design of the adaptive observer is based upon a dynamic state model of tool wear in turning. This paper (Part I) presents the model, and explains its use as the basis for the adaptive observer design. This model uses flank wear and crater wear as state variables, feed as the input, and the cutting force as the output. The suitability of the model as the basis for adaptive observation is also verified. The implementation of the adaptive observer requires the design of a state observer and a parameter estimator. To obtain the model parameters for tuning the adaptive observer procedures for linearisation of the non-linear model are specified. The implementation of the adaptive observer in turning and experimental results are presented in a companion paper (Part II).Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/26748/1/0000300.pd

Model-based sensor location selection for helicopter gearbox monitoring

A new methodology is introduced to quantify the significance of accelerometer locations for fault diagnosis of helicopter gearboxes. The basis for this methodology is an influence model which represents the effect of various component faults on accelerometer readings. Based on this model, a set of selection indices are defined to characterize the diagnosability of each component, the coverage of each accelerometer, and the relative redundancy between the accelerometers. The effectiveness of these indices is evaluated experimentally by measurement-fault data obtained from an OH-58A main rotor gearbox. These data are used to obtain a ranking of individual accelerometers according to their significance in diagnosis. Comparison between the experimentally obtained rankings and those obtained from the selection indices indicates that the proposed methodology offers a systematic means for accelerometer location selection

Detection of Damage in Operating Wind Turbines by Signature Distances

Wind turbines operate in the atmospheric boundary layer and are subject to complex random loading. This precludes using a deterministic response of healthy turbines as the baseline for identifying the effect of damage on the measured response of operating turbines. In the absence of such a deterministic response, the stochastic dynamic response of the tower to a shutdown maneuver is found to be affected distinctively by damage in contrast to wind. Such a dynamic response, however, cannot be established for the blades. As an alternative, the estimate of blade damage is sought through its effect on the third or fourth modal frequency, each found to be mostly unaffected by wind. To discern the effect of damage from the wind effect on these responses, a unified method of damage detection is introduced that accommodates different responses. In this method, the dynamic responses are transformed to surfaces via continuous wavelet transforms to accentuate the effect of wind or damage on the dynamic response. Regions of significant deviations between these surfaces are then isolated in their corresponding planes to capture the change signatures. The image distances between these change signatures are shown to produce consistent estimates of damage for both the tower and the blades in presence of varying wind field profiles

An Adaptive Observer for Online Tool Wear Estimation in Turning (Automation).

On-line sensing of tool wear has been a long-st and ing goal of the manufacturing engineering community. Due to the absence of any reliable on-line tool wear sensors a new model-based approach for tool wear estimation has been proposed. This approach is an adaptive observer which uses both parameter and state estimation techniques. The adaptive observer is based upon a dynamic model which describes the state of wear of the tool. The nonlinear model, which is developed from available relationships in the manufacturing literature, uses flank wear and crater wear as state variables, feed as the input, and the cutting force as the output. The model's general behavior, studied by simulation, agrees well with the results reported in the manufacturing literature. Also, the suitability of the model for parameter and state estimation is investigated by linearizing the model and studying its controllability and observability conditions. The results indicate that the model is suitable for tool wear estimation. The adaptive observer is tuned for the nonlinear model to produce fairly good estimates. In order to reconstruct the wear components from the estimated states, a transformation matrix which can only be obtained by off-line estimation is required. This transformation is approximated by regression analysis (for simulation purposes only) to investigate the suitability of the adaptive observer for on-line tool wear estimation. Despite the close approximation obtained for this transformation matrix, due to numerical problems, the reconstructed wear components from the observed states are not accurate for multi-wear cases. However, for cases where only one type of wear is dominant the method gives good results. In these cases, the reconstruction of the wear component relies on a transformation component which can be obtained by the off-line estimation of a single coefficient. The method is implemented in a real turning operation for cases where only one type of wear is dominant. The laboratory experiments give excellent results in the flank wear only case, whereas the estimation results in the crater wear only case are acceptable but not as satisfactory.Ph.D.Mechanical engineeringUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/161083/1/8621269.pd