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 mployed 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 ensor as in-situ bearing defect identification

Application of acoustic emission in diagnostic of bearing faults within a helicopter gearbox

AbstractAcoustics Emissions (AE) technology has emerged as a promising diagnostic approach. AE was originally developed for non-destructive testing of static structures, however, in recent times its application has been extended to health monitoring of rotating machines. This paper introduces a novel method for application of AE in monitoring of helicopter gearboxes. In addition this paper investigates the application of signal separation techniques in detection of bearing faults within the epicyclic module of a large helicopter (CS-29) main gearbox using Acoustic Emissions (AE). The results showed successful of AE in detection bearing fault within the helicopter gearbox. Detection of the small bearing defect gives the AE an indisputable diagnosis advantage and prove ability of application of AE in helicopter gearboxes

Planetary bearing defect detection in a commercial helicopter main gearbox with vibration and acoustic emission

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Helicopter gearboxes significantly differ from other transmission types and exhibit unique behaviors that reduce the effectiveness of traditional fault diagnostics methods. In addition, due to lack of redundancy, helicopter transmission failure can lead to catastrophic accidents. Bearing faults in helicopter gearboxes are difficult to discriminate due to the low signal to noise ratio (SNR) in the presence of gear vibration. In addition, the vibration response from the planet gear bearings must be transmitted via a time-varying path through the ring gear to externally mounted accelerometers, which cause yet further bearing vibration signal suppression. This research programme has resulted in the successful proof of concept of a broadband wireless transmission sensor that incorporates power scavenging whilst operating within a helicopter gearbox. In addition, this paper investigates the application of signal separation techniques in detection of bearing faults within the epicyclic module of a large helicopter (CS-29) main gearbox using vibration and Acoustic Emissions (AE). It compares their effectiveness for various operating conditions. Three signal processing techniques including an adaptive filter, spectral kurtosis and envelope analysis, were combined for this investigation. In addition, this research discusses the feasibility of using AE for helicopter gearbox monitoring

A comparative study of the effectiveness of vibration and acoustic emission in diagnosing a defective bearing in a planetry gearbox

Whilst vibration analysis of planetary gearbox faults is relatively well established, the application of Acoustic Emission (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 to reduce the effect of background noise whilst extracting the fault feature from AE and vibration signatures. This has been achieved through developing of internal AE sensor for helicopter transmission system. In addition, series of signal processing procedure has been developed to improved detection of incipient damage. 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 pat

Condition Indicators for Gearbox Condition Monitoring Systems

Condition monitoring systems for manual transmissions based on vibration diagnostics are widely applied in industry. The systems deal with various condition indicators, most of which are focused on a specific type of gearbox fault. Frequently used condition indicators (CIs) are described in this paper. The ability of a selected condition indicator to describe the degree of gearing wear was tested using vibration signals acquired during durability testing of manual transmission with helical gears.

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

Bearing Signal Separation of Commercial Helicopter Main Gearbox

Gears are significant component in a multiplicity of industrial applications such as machine tool and gearboxes. An unforeseen failure of gear may result in significant economic losses. Therefore this research propose fault detection improvement throught series of vibration signal processing techuiques. These techniques have been tested experimentally using vibration data collected from the transmission system of a CS-29 ‘Category A’ helicopter gearbox under different bearing damage severity of the second planetary stage. Results showed successful improvement of bearing fault detection

Bearing signal separation enhancement with application to helicopter transmission system

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Bearing vibration signal separation is essential for fault detection of gearboxes, especially where the vibration is nonstationary, susceptible to background noise, and subjected to an arduous transmission path from the source to the receiver. This paper presents a methodology for improving fault detection via a series of vibration signal processing techniques, including signal separation, synchronous averaging (SA), spectral kurtosis (SK), and envelope analysis. These techniques have been tested on experimentally obtained vibration data acquired from the transmission system of a CS-29 Category A helicopter gearbox operating under different bearing damage conditions. Results showed successful enhancement of bearing fault detection on the second planetary stage of the gearbo

A review of physics-based models in prognostics: application to gears and bearings of rotating machinery

Health condition monitoring for rotating machinery has been developed for many years due to its potential to reduce the cost of the maintenance operations and increase availability. Covering aspects include sensors, signal processing, health assessment and decision-making. This article focuses on prognostics based on physics-based models. While the majority of the research in health condition monitoring focuses on data-driven techniques, physics-based techniques are particularly important if accuracy is a critical factor and testing is restricted. Moreover, the benefits of both approaches can be combined when data-driven and physics-based techniques are integrated. This article reviews the concept of physics-based models for prognostics. An overview of common failure modes of rotating machinery is provided along with the most relevant degradation mechanisms. The models available to represent these degradation mechanisms and their application for prognostics are discussed. Models that have not been applied to health condition monitoring, for example, wear due to metal–metal contact in hydrodynamic bearings, are also included due to its potential for health condition monitoring. The main contribution of this article is the identification of potential physics-based models for prognostics in rotating machinery

An investigation on the diagnostics and prognostic capabilities of acoustic emission (AE) on a spur gearbox

The aim of this research project is to explore a new technique, Acoustic Emission (AE), on both the diagnostic and prognostic capabilities in monitoring gear teeth degradation (pitting), and compare with the more widely used techniques such as vibration monitoring and Spectrometric Oil Analysis (SOA). Furthermore, by employing the experimental results and past literature, a model in predicting the amount of gear surface pitting wear using AE activity level was proposed. The successful forinulation of this proposed model may be able to predict the remaining life of the gear after pitting has been detected, thereby allowing timely replacement to be carried out without the risk of catastrophic failure. A series of experimental tests which include seeded defect simulations, study on the effect of operating parameters over AE (under isothermal conditions), AE source determination tests and accelerated gear fatigue tests have been performed to investigate the diagnostics and prognostics capabilities of AE via a back-to-back gearbox set up. The experimental results achieved have highlighted some significant findings: (a) The variation in rotating speeds, change the AE levels in a much significant amount as compared to the same variation in applied load. (b) The prime source of AE was postulated to be asperity contact under rolling and sliding of the meshing gear teeth surfaces. (c) AE technique has a far better degradation (pitting) monitoring capability compared to vibration and SOA techniques. These findings have made a vast contribution in condition monitoring of gearbox using AE technique and the proposed model has also offered opportunity to make AE a potentially viable and effective tool in diagnosis and prognosis of gearbox or even other rotating machinery defects.EThOS - Electronic Theses Online ServiceGBUnited Kingdo