Evaluation of gear pitting severity by using various condition monitoring indicators

Fault detection techniques based on vibration measurement are implemented to identify in an early stage failures appearing in gear transmissions. Condition monitoring indicators (CMI), like: Root Mean Square (RMS), Crest Factor, Kurtosis, FMO, FM4, Energy ratio, Energy operator, NA4 or NB4, are used to estimate the level of gear faults such as pitting, cracks, spalling, scuffing or scoring. However, in is multitude of indicators, the question that arises is: which CMI is the most sensitive in estimating the severity of defects? Thus, this paper presents an extensive comparison between the before mentioned indicators computed from vibration signals collected on four pinions with different pitting grades, created by artificial means. The pinions where incorporated in a single helical gearbox and the tests were performed on an open-energy test rig at three different input speeds. This comparative study assesses the receptivity of different condition monitoring indicators towards gear pitting failure

Evaluation of gear pitting severity by using various condition monitoring indicators

Fault detection techniques based on vibration measurement are implemented to identify in an early stage failures appearing in gear transmissions. Condition monitoring indicators (CMI), like: Root Mean Square (RMS), Crest Factor, Kurtosis, FMO, FM4, Energy ratio, Energy operator, NA4 or NB4, are used to estimate the level of gear faults such as pitting, cracks, spalling, scuffing or scoring. However, in is multitude of indicators, the question that arises is: which CMI is the most sensitive in estimating the severity of defects? Thus, this paper presents an extensive comparison between the before mentioned indicators computed from vibration signals collected on four pinions with different pitting grades, created by artificial means. The pinions where incorporated in a single helical gearbox and the tests were performed on an open-energy test rig at three different input speeds. This comparative study assesses the receptivity of different condition monitoring indicators towards gear pitting failure

Enhancement of Gear Fault Detection Using Narrowband Interference Cancellation

The development of enhanced fault detection ability for gearbox systems has received considerable attention in recent years. Detecting the gear fault easier is very important for maintenance action. This has driven the need in research for enhanced gear fault detection method. The goal is to extract periodic impulse signal from the very noise signal which mainly contains the narrowband signals. This can be done by enhancing the impulsive signals while suppressing the narrowband signals. This paper used a new method, Narrowband Interference Cancellation, to detect the gear fault. This method reserves the impulsive signal produced by gear fault and removes the other signals out. The methodology is demonstrated on a gearbox run-to-failure test. The results show that Narrowband Interference Cancellation can enable the gear fault detection easier

Research on vibration characteristics of marine power-spilt gear system

In order to carry out a more in-depth study on the vibration characteristics of the marine power-spilt gear system, a nonlinear dynamic model of a certain type of marine gearbox was established, taking into account factors such as dynamic backlash and time-varying mesh stiffness. The numerical simulation method was used to calculate the variation of the system vibration response with input speed, the torsional stiffness of the linkage shaft and the stiffness of the output shaft under different backlach. The results show that the system will occur resonance as the input speed increases; the torsional stiffness of the linkage axis has an optimal value, so that the vibration response amplitude of the system is the smallest; as the support stiffness of the output shaft increases, the vibration acceleration of the output shaft does not change much, while the vibration displacement is significantly reduced

Chromatic monitoring of gear mechanical degradation based on acoustic emission

This paper presents a methodology for the feature estimation of a new fault indicator focused on detecting gear mechanical degradation under different operating conditions. Preprocessing of acoustic emission signal is performed by applying chromatic transformation to highlight characteristic patterns of the mechanical degradation. In this study, chromaticity based on the computation of the hue, light, and saturation transformation of the main acoustic emission intrinsic mode functions is performed. Then, a topology preservation approach is carried out to describe the chromatic signature of the healthy gear condition. Thus, the detection index can be estimated. It must be noted that the applied chromatic monitoring process only requires the characterization of the healthy gear condition, being applicable to a wide range of operating conditions of the gear. Performance of the proposed system is validated experimentally. According to the obtained results, the proposed methodology is reliable and feasible for monitoring gear mechanical degradation in industrial applications.Peer ReviewedPostprint (published version

Investigation of Spiral Bevel Gear Condition Indicator Validation via AC-29-2C Combining Test Rig Damage Progression Data with Fielded Rotorcraft Data

This is the final of three reports published on the results of this project. In the first report, results were presented on nineteen tests performed in the NASA Glenn Spiral Bevel Gear Fatigue Test Rig on spiral bevel gear sets designed to simulate helicopter fielded failures. In the second report, fielded helicopter HUMS data from forty helicopters were processed with the same techniques that were applied to spiral bevel rig test data. Twenty of the forty helicopters experienced damage to the spiral bevel gears, while the other twenty helicopters had no known anomalies within the time frame of the datasets. In this report, results from the rig and helicopter data analysis will be compared for differences and similarities in condition indicator (CI) response. Observations and findings using sub-scale rig failure progression tests to validate helicopter gear condition indicators will be presented. In the helicopter, gear health monitoring data was measured when damage occurred and after the gear sets were replaced at two helicopter regimes. For the helicopters or tails, data was taken in the flat pitch ground 101 rotor speed (FPG101) regime. For nine tails, data was also taken at 120 knots true airspeed (120KTA) regime. In the test rig, gear sets were tested until damage initiated and progressed while gear health monitoring data and operational parameters were measured and tooth damage progression documented. For the rig tests, the gear speed was maintained at 3500RPM, a one hour run-in was performed at 4000 in-lb gear torque, than the torque was increased to 8000 in-lbs. The HUMS gear condition indicator data evaluated included Figure of Merit 4 (FM4), Root Mean Square (RMS) or Diagnostic Algorithm 1(DA1), + 3 Sideband Index (SI3) and + 1 Sideband Index (SI1). These were selected based on their sensitivity in detecting contact fatigue damage modes from analytical, experimental and historical helicopter data. For this report, the helicopter dataset was reduced to fourteen tails and the test rig data set was reduced to eight tested gear sets. The damage modes compared were separated into three cases. For case one, both the gear and pinion showed signs of contact fatigue or scuffing damage. For case two, only the pinion showed signs of contact fatigue damage or scuffing. Case three was limited to the gear tests when scuffing occurred immediately after the gear run-in. Results of this investigation highlighted the importance of understanding the complete monitored systems, for both the helicopter and test rig, before interpreting health monitoring data. Further work is required to better define these two systems that include better state awareness of the fielded systems, new sensing technologies, new experimental methods or models that quantify the effect of system design on CI response and new methods for setting thresholds that take into consideration the variance of each system

Prognostic-based Life Extension Methodology with Application to Power Generation Systems

Practicable life extension of engineering systems would be a remarkable application of prognostics. This research proposes a framework for prognostic-base life extension. This research investigates the use of prognostic data to mobilize the potential residual life. The obstacles in performing life extension include: lack of knowledge, lack of tools, lack of data, and lack of time. This research primarily considers using the acoustic emission (AE) technology for quick-response diagnostic. To be specific, an important feature of AE data was statistically modeled to provide quick, robust and intuitive diagnostic capability. The proposed model was successful to detect the out of control situation when the data of faulty bearing was applied. This research also highlights the importance of self-healing materials. One main component of the proposed life extension framework is the trend analysis module. This module analyzes the pattern of the time-ordered degradation measures. The trend analysis is helpful not only for early fault detection but also to track the improvement in the degradation rate. This research considered trend analysis methods for the prognostic parameters, degradation waveform and multivariate data. In this respect, graphical methods was found appropriate for trend detection of signal features. Hilbert Huang Transform was applied to analyze the trends in waveforms. For multivariate data, it was realized that PCA is able to indicate the trends in the data if accompanied by proper data processing. In addition, two algorithms are introduced to address non-monotonic trends. It seems, both algorithms have the potential to treat the non-monotonicity in degradation data. Although considerable research has been devoted to developing prognostics algorithms, rather less attention has been paid to post-prognostic issues such as maintenance decision making. A multi-objective optimization model is presented for a power generation unit. This model proves the ability of prognostic models to balance between power generation and life extension. In this research, the confronting objective functions were defined as maximizing profit and maximizing service life. The decision variables include the shaft speed and duration of maintenance actions. The results of the optimization models showed clearly that maximizing the service life requires lower shaft speed and longer maintenance time