Eric Bechhoefer

Gearbox Tooth Cut Fault Diagnostics Using Acoustic Emission and Vibration Sensors — A Comparative Study

In recent years, acoustic emission (AE) sensors and AE-based techniques have been developed and tested for gearbox fault diagnosis. In general, AE-based techniques require much higher sampling rates than vibration analysis-based techniques for gearbox fault diagnosis. Therefore, it is questionable whether an AE-based technique would give a better or at least the same performance as the vibration analysis-based techniques using the same sampling rate. To answer the question, this paper presents a comparative study for gearbox tooth damage level diagnostics using AE and vibration measurements, the first known attempt to compare the gearbox fault diagnostic performance of AE- and vibration analysis-based approaches using the same sampling rate. Partial tooth cut faults are seeded in a gearbox test rig and experimentally tested in a laboratory. Results have shown that the AE-based approach has the potential to differentiate gear tooth damage levels in comparison with the vibration-based approach. While vibration signals are easily affected by mechanical resonance, the AE signals show more stable performance.

Gear Fault Location Detection for Split Torque Gearbox Using AE Sensors

In comparison with a traditional planetary gearbox, the split torque gearbox (STG) potentially offers lower weight, increased reliability, and improved efficiency. These benefits have driven helicopter object exchange models (OEMs) to develop products using STG. However, the unique structure of the STG creates a problem on how to locate the gear faults in an STG. As of today, only limited research on STG fault detection using vibration and acoustic emission (AE) sensors has been conducted. In this paper, an effective gear fault location detection methodology using AE sensors for STG is presented. The methodology uses wavelet transform to process AE sensor signals at different locations to determine the arrival time of the AE bursts. By analyzing the arrival time of the AE bursts, the gear fault location can be determined. The parameters of the wavelets are optimized by using an ant colony optimization algorithm. Real seeded gear fault experimental tests on a notional STG are conducted. AE signals at different locations of the gearbox with both healthy and damaged output driving gears are collected simultaneously to determine the location of the damaged gear. Experimental results have shown the effectiveness of the presented methodology.

Bearing envelope analysis window selection Using spectral kurtosis techniques

Envelope Analysis is a well-known signal processing technique for bearing fault detection. However, improper window selection can result in poor fault detection performance. Using a known fault data set, we quantify the performance of spectral kurtosis (SK) and envelope kurtosis (EK) as a technique for setting an optimal frequency and bandwidth window for the envelope analysis. We establish a measure of effectiveness (MOE): the correlation of fault energy with total spall length. With this MOE, we evaluate the ability of SK/EK to predict the optimal envelope analysis window.

A Generalized Process for Optimal Threshold Setting in HUMS

Monitoring the health of a helicopter drive train enhances flight safety and reduces operating costs. Health and usage management systems (HUMS) monitor the drive train by using accelerometers to measure component vibration. Algorithms process the time domain vibration data into various condition indicators (CI), which are used to determine component health via thresholding. For the rotating machinery, a standard set of CI are shaft order one, two and three (i.e. 1, 2 or 3 times the shaft RPM). Shaft order one (SOI) is indicative of an unbalance, where as higher shaft order can be used to detect a bent shaft or misalignment condition. In the case of bearings, CIs are envelope spectrum or cepstrum analysis of the ball, cage, inner race and outer race frequencies. There are a number of standard CI used for gear analysis, such as line elimination and resynthesis, side band modulation, gear misalignment, etc. In general, some method is used to set thresholds for these CIs: when the threshold is exceeded, maintenance is recommended. The HUMS system must balance the risk of setting the threshold too high such that a component may fail in flight versus the risk of setting the threshold too low, which results in additional maintenance cost. This paper covers a generalized process of optimally setting threshold for CI and fusing the information into an Health Indicator. It can be shown that the distributions of CI for shaft magnitude and bearing envelop energy are Rayleigh distribution. The normalized distance functions for these CIs are a Nakagami distribution with mu (shape parameter) of n (number of CI) and Omega (scale parameter) of 2 x 1/(2-pi/2) x mu. For gear CIs, which are considered as Gaussian, the normalized distance function is again Nakagami, but with a mu of nil and Omega of n. Given the theoretical mu and Omega, a threshold for any set of CI can be generated resulting in system probability of false alarm (PFA). This is an optimal decision rule for detecting a component which is no longer nominal. The normalized distance distribution is a function of the component CI sample statistic. Procedures are developed to calculate the unbiased statistic: covariance for Rayleigh based CIs and mean value/covariance for Gaussian based CIs. In the cases where the population of components is not nominal (e.g. mass imbalances which violate the Rayleigh assumption) tools are presented to control this. For gear, normalizing transforms can be used to ensure the CIs are more Gaussian. Example data from utility helicopters are given.

Quantification of condition indicator performance on a split torque gearbox

The requirement for higher energy density transmissions (lower weight) in helicopters has led to the development of the split torque gearbox (STG) to replace the traditional planetary gearbox by the drive train designer. This may pose a challenge for the current gear analysis methods used in health and usage monitoring systems (HUMS). Gear analysis uses time synchronous averages to separates in frequency gears that are physically close to a sensor. The effect of a large number of synchronous components (gears or bearing) in close proximity may significantly reduce the fault signal (reduce signal to noise ratio) and therefore reduce the effectiveness of current gear analysis algorithms. In this paper, quantification of condition indicator performance on a split torque gearbox is reported. The vibration signatures are processed through a number of gear analysis algorithms to quantify the gear fault performance. The performance metric is separability.

Application of the condition based maintenance checking system for aircrafts

Condition Based Maintenance (CBM) systems have evolved as an effective health and usage monitoring mechanism in aircrafts by reducing the costs associated with unscheduled maintenance. CBM systems help maintainers to detect and manage the condition of aviation system components and take maintenance actions when there is evidence of need. In this paper we describe the application of a software prototype, which is an automation of the CBM practices. We briefly explain the novel framework on which it is built. We illustrate the building of the configuration information, required to generate the maintenance reports that are deemed essential for continuous improvement under CBM, using a markup language called XML. We explain the procedure in generating the reports using the developed prototype. We demonstrate that the developed prototype has the functional capabilities essential to implement CBM on any aircraft and is valuable in cutting down the software maintenance costs as it can perform new operations without having to modify the existing source codes.

Use of Paris Law for Prediction of Component Remaining Life

Vibration based health and usage monitoring systems (HUMS) are providing good information as to the current state of a component, (e.g. health), but other than simple trending, are not robust enough to yield an estimated of the remaining useful life (RUL). There are three fundamental problems that need to be address to accurately estimate RUL. First, health index data is noisy. Even with filtering, the HI can be difficult to trend. Second - a damage model is required address component degradation. Finally, a relationship between physical damage and measured health/condition needs to be established.

Mechanical Diagnostics System Engineering in IMD HUMS

The Goodrich Integrated Mechanical Diagnostics Health and Usage System (IMD-HUMS) mechanical diagnostics functionality is the integration of disparate subsystems. When the aircraft is in the appropriate capture window, the primary processing unit (PPU), commands the vibration processing unit (VPU) to capture vibration data and a tachometer reference. This time domain data is processed by standard and proprietary algorithms to generate component condition indicators (CI). These CI are statistics, which when used with a priori configuration data, are mapped into component health indicators (HI). The VPU passes the component HI data to both the PPU and the data transfer unit for ground station display. The PPU, taking the HI data for a component, can determine if the component has a degraded health state. If the component is degraded, the PPU can generate an exceedance message to be reviewed during maintenance debrief. After the flight, all CI and HI data are stored in a data base and is available for display against an aircraft, composite component (e.g. line replaceable unit) and the component itself. The acquisition process is complicated by noise from internal sources (non synchronous gears, shafts and bearing not under analysis) and external sources (changes in airspeed, torque, weight, etc). The HI becomes, in essence, a statistical indicator of the components health. As such, the best estimator of component health is calculated using a Kalman filter. This reduces variance in the data prior to display of the component HI to the aircraft operator. This filtered HI is called the DHI (display health indicator). The DHI uses a priori information and sampling theory to build the best available representation of health of the component. This paper addresses the system engineering required to integrate the vibration processing, decision algorithms, thresholding and filtering to give the operator the best representation of component health. The integration of the system allows IMD-HUMS to have a high degree of certainty in the information given to the operator. This information could potentially improve maintenance practices, lowering aircraft operating cost while improving aircraft safety. The system engineering insures that the recommendation for component maintenance has a low probability of false alarm while maintaining a high probability of component fault detection

Stochastic modeling of damage physics for mechanical component prognostics using condition indicators

The health of a mechanical component deteriorates over time and its service life is randomly distributed and can be modeled by a stochastic deterioration process. For most of the mechanical components, the deterioration process follows a certain physical laws and their mean life to failure can be determined approximately by these laws. However, it is not easy to apply these laws for mechanical component prognostics in current health monitoring applications. In this paper, a stochastic modeling methodology for mechanical component prognostics with condition indicators used in current health monitoring applications is presented. The effectiveness of the methodology is demonstrated with a real shaft fatigue life prediction case study.

Split torque type gearbox fault detection using acoustic emission and vibration sensors

In comparison with a traditional planetary gearbox, the split torque gearbox (STG) potentially offers lower weight, increased reliability, and improved efficiency. These benefits have driven the helicopter OEMs to develop products using the STG. However, this may pose a challenge for the current gear analysis methods used in Health and Usage Monitoring Systems (HUMS). Gear analysis uses time synchronous averages to separates in frequency gears that are physically close to a sensor. The effect of a large number of synchronous components (gears or bearing) in close proximity may significantly reduce the fault signal (decreased signal to noise) and therefore reduce the effectiveness of current gear analysis algorithms. As of today, only a limited research on STG fault diagnosis using vibration sensors has been conducted. In this paper, an investigation on STG fault detection using both vibration and acoustic emission (AE) sensors is reported. In particular, signals of both vibration and AE sensors on a notational STG type gearbox were collected from seeded fault tests. Gear fault features were extracted from vibration signals using a Hilbert-Huang Transform (HHT) based algorithm and from AE signals using AE analysis. These fault features were input to a K-nearest neighbor (KNN) algorithm for fault detection. The investigation results showed that both vibration and AE sensors were capable of detecting the gear fault in a STG. However, in terms of locating the source of the fault, AE sensors outperformed vibration sensors.