nan David

Observations of acoustic emission activity during gear defect diagnosis

It is widely recognised that acoustic emission (AE) is gaining ground as a non-destructive technique (NDT) for health diagnosis on rotating machinery. The source of AE is attributed to the release of stored elastic energy that manifests itself in the form of elastic waves that propagate in all directions on the surface of a material. These detectable AE waves can provide useful information about the health condition of a machine. This paper reports on part of an ongoing experimental investigation on the application of AE for gear defect diagnosis. Furthermore, the possibility of monitoring gear defects from the bearing casing is examined. It is concluded that AE offers a complimentary tool for health monitoring of gears.

A Comparative Study of the Effectiveness of Adaptive Filter Algorithms, Spectral Kurtosis and Linear Prediction in Detection of a Naturally Degraded Bearing in a Gearbox

AbstractDiagnosing bearing faults at the earliest stages is critical in avoiding future catastrophic failures. Many techniques have been developed and applied in diagnosing bearings faults; however, these traditional diagnostic techniques are not always successful when the bearing fault occurs in gearboxes where the vibration response is complex; under such circumstances, it may be necessary to separate the bearing signal from the complex signal. In this paper, an adaptive filter has been applied for the purpose ofn bearing signal separation. Four algorithms were compared to assess their effectiveness in diagnosing a bearing defect in a gearbox, least mean square (LMS), linear prediction, spectral kurtosis and fast block LMS. These algorithms were applied to decompose the measured vibration signal into deterministic and random parts with the latter containing the bearing signal. These techniques were applied to identify a bearing fault in a gearbox employed for an aircraft control system for which endurance tests were performed. The results show that the LMS algorithm is capable of detecting the bearing fault earlier in comparison with the other algorithms.

Bearing time-to-failure estimation using spectral analysis features

With the increasing use of health usage monitoring systems on helicopters, a lot of research has been undertaken for diagnosis of transmission components. However, most of these works are performed in laboratory environments and there are hardly any published works on in-service application. In this study, we present an experience in diagnosis of a helicopter gearbox bearing using actual service data gathered from AH64D helicopters belonging to the Republic of Singapore Air Force. A number of helicopters have been found with grease leak and radial play in the tail rotor gearbox output shaft during field maintenance. Subsequent tear-down inspections of the tail rotor gearboxes revealed that they had similar defects of bearing race spalling and widespread pitting of the rolling elements. Spectral analysis was carried out on the accelerometer data from these helicopters and correlated with the tear-down inspection findings. The fault patterns exhibited correspond well to progressing stages of bearing wear and are consistent across defective gearboxes from different helicopters. It is demonstrated that simple spectral analysis can be effective in tracking progressive stages of bearing damage using both low-frequency and high-frequency bandwidths. The observed fault patterns are extracted as features for diagnosis and used to determine the bearings’ estimated time to failure for maintenance planning.

Diagnostics and prognostics using switching Kalman filters

The use of condition monitoring data for diagnostic and prognostic of vehicle health has been growing with increasing use of health and usage monitoring systems. In this article, an approach using the switching Kalman filter framework is explored for both diagnostic and prognostic using condition monitoring data under a single framework. The switching Kalman filter uses multiple dynamical models each describing a different degradation process. The most probable underlying degradation process is then inferred from the observed condition monitoring data using Bayesian estimation. By using the dynamical behavior of the degradation process, pre-established fault detection threshold is no longer required. This approach also provides maintainers with more information for decision-making as a probabilistic measure of the degradation processes is available. This helps maintainers to predict remaining useful life more accurately by distinguishing between the degradation states and performing prediction only when unstable degradation is detected. The proposed switching Kalman filter approach is applied onto sets of condition monitoring data from gearbox bearings that were found defective from the Republic of Singapore Air Force AH64D helicopter. The use of in-service data in a practical scenario shows that the switching Kalman filter approach is a promising tool for maintenance decision-making.

Monitoring oil film regimes with acoustic emission

The major purpose of a gear lubricant is to provide adequate oil film thickness to reduce and prevent gear tooth surface failures. Real-time monitoring for gear failures is important in order to predict and prevent unexpected failures which would have a negative impact on the efficiency, performance and safety of the gearbox. This paper presents experimental results on the influence of specific oil film thickness on acoustic emission activity for operational helical gears. Variation in film thickness during operations was achieved by spraying liquid nitrogen onto the rotating gear wheel. The experimental results demonstrated a clear relationship between the root mean square value of the acoustic emission signal and the specific film thickness. The findings demonstrate the potential of acoustic emission technology to quantify lubrication regimes on operational gears.

Investigation on Sand Particle Impingement on Steel Pipe in Two Phase Flow Using Acoustic Emission Technology

This paper presents an experimental investigation that employed the acoustic emission (AE) technology to monitor sand transportation in two-phase flow. This investigation was undertaken on two phase (air-sand) flow in a horizontal pipe for varying Superficial Gas Velocities (VSG). The objective of this research programme is to develop a simple, non-invasive technique for monitoring of sand particle concentration levels in multi-phase flow conditions. The experimental findings show that AE absolute energy can be correlated with the size of sand, number of sand particles and Superficial Gas Velocity (VSG).

Multidimensional prognostics for rotating machinery: A review

Determining prognosis for rotating machinery could potentially reduce maintenance costs and improve safety and availability. Complex rotating machines are usually equipped with multiple sensors, which enable the development of multidimensional prognostic models. By considering the possible synergy among different sensor signals, multivariate models may provide more accurate prognosis than those using single-source information. Consequently, numerous research papers focusing on the theoretical considerations and practical implementations of multivariate prognostic models have been published in the last decade. However, only a limited number of review papers have been written on the subject. This article focuses on multidimensional prognostic models that have been applied to predict the failures of rotating machinery with multiple sensors. The theory and basic functioning of these techniques, their relative merits and drawbacks and how these models have been used to predict the remnant life of a machine are discussed in detail. Furthermore, this article summarizes the rotating machines to which these models have been applied and discusses future research challenges. The authors also provide seven evaluation criteria that can be used to compare the reviewed techniques. By reviewing the models reported in the literature, this article provides a guide for researchers considering prognosis options for multi-sensor rotating equipment.

Diagnostics of a Defective Bearing Within a Planetary Gearbox with Vibration and Acoustic Emission

Whilst vibration analysis of planetary gearbox faults is relatively well established, the application of Acoustic Emissions (AE) to this field is still in its infancy. For planetary-type gearboxes it is more challenging to diagnose bearing faults due to the dynamically changing transmission paths which contribute to masking the vibration signature of interest. The present study is aimed at developing a series of signal processing procedures to reduce the effect of background noise whilst extracting the fault feature from AE and vibration signatures. Three signal processing techniques including an adaptive filter, spectral kurtosis and envelope analysis, were applied to AE and vibration data acquired from a simplified planetary gearbox test rig with a seeded bearing defect. The results show that AE identified the defect earlier than vibration analysis irrespective of the tortuous transmission path.

Helicopter main gearbox bearing defect identification with acoustic emission techniques

Helicopter transmission integrity is critical to the safety operation. Among all mechanical failures in helicopter transmission, the main gearbox (MGB) failures occupy approximately 16%. Great effort has been paid in early prevention and diagnosis of MGB failures. As a commonly employed monitoring technology, vibration analysis suffers from strong background noise due to variable transmission paths from the bearing to the receiving externally mounted vibration sensor. The background noise can mask the signal signature of interest. This paper reports on an investigation to identify the presence of a bearing defect in a CS29 Category `A helicopter main gearbox with acoustic emission (AE) technologies. This investigation involved performing the tests for faultfree condition, minor bearing damage and major bearing damage conditions under different power levels. The bearing faults were seeded on one of the planet gears of the second epicyclic stage. To overcome the issue of low signal to noise ratio (SNR), AE sensor was directly attached on the dish of planet carrier. The AE signal was transferred wireless to avoid complex wiring inside MGB. The analysis results proved the feasibility of using AE sensor as in-situ bearing defect identification.

Slug velocity measurement using acoustic emission technology

Two-phase flow is a common phenomenon that exists in the petroleum and chemical engineering industrial fields. An important feature employed to describe two-phase flow is the flow regime which varies depending on the individual velocities of the components within the two-phase flow. One of these regimes, the slug regime, can create significant pressure fluctuations that compromise the integrity of the transporting structure (pipes, separators, etc). This is in addition to other unwanted effects such as flooding at the receiving end, an increase in deposits of hydrates and corrosion. Under such circumstances, the detection of the slug and its associated characteristics are vitally important to the operator. This experimental study looks into the application of acoustic emission (AE) technology for detecting slug velocity in two phase (gas/liquid) flow. It is concluded that the slug velocity can be determined with AE sensors. The results were validated by using Ultrasound Transit Time technique and there was a good agreement between the two techniques at low gas void fraction.