A study on helicopter main gearbox planetary bearing fault diagnosis

The condition monitoring of helicopter main gearbox (MGB) is crucial for operation safety, flight airworthiness and maintenance scheduling. Currently, the helicopter health and usage monitoring system, HUMS, is installed on helicopters to monitor the health state of their transmission systems and predict remaining useful life of key helicopter components. However, recent helicopter accidents related to MGB failures indicate that HUMS is not sensitive and accurate enough to diagnose MGB planetary bearing defects. To contribute in improving the diagnostic capability of HUMS, diagnosis of a MGB planetary bearing with seeded defect was investigated in this study. A commercial SA330 MGB was adopted for the seeded defect tests. Two test cases are demonstrated in this paper: the MGB at 16,000 rpm input speed with 180 kW load and at 23,000 rpm input speed with 1760 kW load. Vibration data was recorded, and processed using signal processing techniques including self-adaptive noise cancellation (SANC), kurtogram and envelope analysis. Processing results indicate that the seeded planetary bearing defect was successfully detected in both test cases

Towards Transparency of IoT Message Brokers

In this paper we propose an ontological model for documenting provenance of MQTT message brokers to enhance the transparency of interactions between IoT agents

Build an app and they will come? Lessons learnt from trialling the GetThereBus app in rural communities

Acknowledgements The research described here was supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub; award reference: EP/G066051/1.Peer reviewedPostprin

Using empirical mode decomposition scheme for helicopter main gearbox bearing defect identification

© 2016 IEEE. Vibration sensors for helicopter health and condition monitoring have been widely employed to ensure the safe operation. Through the years, vibration sensors are now commonly placed on helicopters and have claimed a number of successes in preventing accidents. However, vibration based bearing defect identification remains a challenge since bearing defects signatures are usually contaminated by background noise resulting from variable transmission paths from the bearing to the receiving externally mounted vibration sensors. In this paper, the empirical mode decomposition (EMD) scheme was utilized to analyze vibration signal captured from a CS29 Category 'A' helicopter main gearbox, where bearing faults were seeded on one of the planetary gears bearing of the second epicyclic stage. The EMD scheme decomposed vibration signal into a number of intrinsic mode functions (IMFs) for subsequent envelope analysis. The selection of appropriate IMFs to characterize bearing fault signatures was discussed. The analysis result showed that the bearing fault signatures were successfully characterized and revealed the efficacy of the EMD scheme

Using independent component analysis scheme for helicopter main gearbox bearing defect identification

© 2017 IEEE. Vibration signal analysis is the most common technique for helicopter health condition monitoring. It has been widely employed to detect helicopter gearbox fault and ensure the safe operation. Through the years, vibration signal analysis has a significant contribution to successfully prevent a number of accidents. However, vibration based bearing identification remains a challenge because bearing defects signatures are contaminated by strong background noise. In this paper, the independent component analysis (ICA) scheme was utilized to analyze vibration signals captured from a CS29 Category 'A' helicopter main gearbox, where bearing faults were seeded on the second epicyclic stage planetary gears bearing. The ICA scheme could separate the multichannel signals into the mutually independent components. The bearing defect signature can be clearly observed in one of the independent components. The analysis result showed that ICA scheme is a promising method for detecting the bearing fault signatures

Helicopter gearbox bearing fault detection using separation techniques and envelope analysis

The main gearbox (MGB) is a crucial part of a helicopter. MGB bearings suffer intensively from stress and friction during flights hence concerns for their health condition and detecting potential defects become critical for the sake of operation safety and system reliability. In this study, bearing defects were seeded in the second epicyclic stage bearing of a commercial Class A helicopter MGB. Vibration and tachometer signals were recorded simultaneously for the purpose of fault diagnosis. The tests were carried out at different power and speed conditions for various seeded bearing defects. This paper presents a comparison of signal processing techniques employed to identify the presence of the defects masked by strong background noise generated from an operation helicopter MGB

Made-up rubbish: design fiction as a tool for participatory Internet of Things research.

As Internet of Things (IoT) technologies become embedded in public infrastructure, it is important that we consider how they may introduce new challenges in areas such as privacy and governance. Public technology implementations can be more democratically developed by facilitating citizen participation during the design process, but this can be challenging. This work demonstrates a novel method for participatory research considering the privacy implications of IoT deployments in public spaces, through the use of world building design fictions. Using three fictional contexts and their associated tangible design fiction objects, we report on findings to inform transparency and governance in public space IoT deployments

Data Quality Assessment and Anomaly Detection Via Map / Reduce and Linked Data: A Case Study in the Medical Domain

Recent technological advances in modern healthcare have lead to the ability to collect a vast wealth of patient monitoring data. This data can be utilised for patient diagnosis but it also holds the potential for use within medical research. However, these datasets often contain errors which limit their value to medical research, with one study finding error rates ranging from 2.3%???26.9% in a selection of medical databases. Previous methods for automatically assessing data quality normally rely on threshold rules, which are often unable to correctly identify errors, as further complex domain knowledge is required. To combat this, a semantic web based framework has previously been developed to assess the quality of medical data. However, early work, based solely on traditional semantic web technologies, revealed they are either unable or inefficient at scaling to the vast volumes of medical data. In this paper we present a new method for storing and querying medical RDF datasets using Hadoop Map / Reduce. This approach exploits the inherent parallelism found within RDF datasets and queries, allowing us to scale with both dataset and system size. Unlike previous solutions, this framework uses highly optimised (SPARQL) joining strategies, intelligent data caching and the use of a super-query to enable the completion of eight distinct SPARQL lookups, comprising over eighty distinct joins, in only two Map / Reduce iterations. Results are presented comparing both the Jena and a previous Hadoop implementation demonstrating the superior performance of the new methodology. The new method is shown to be five times faster than Jena and twice as fast as the previous approach

TravelBot:Utilising Social Media Dialogue to Provide Journey Disruption Alerts

ACKNOWLEDGEMENTS The research described here is supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Research Hub; award reference: EP/G066051/1. We extend our grateful thanks to the participants who have contributed to the studies throughout, and to the industry partner FirstGroup plc for their support.Peer reviewedPublisher PD

Automated Application of Full Matrix Capture to Assess the Structural Integrity of Mooring Chains

In-service mooring chains are subjected to harsh environmental conditions on a daily basis, which increases the necessity of integrity assessment of chain links. Periodic structural health monitoring of mooring chains is mandatory and vital in order to maintain the safety of floating platforms. Applications of ultrasound for in-service mooring chain inspection is still in its infancy due to lack of accessibility, in field operational complexity and the geometrical features of mooring systems. With the advancement of robotic/ automated systems (i.e. chain climbing robotic mechanisms), interest for in-situ ultrasound inspection has increased. Presently, ultrasound inspection has been confined to the weld area of the chain links. However, according to recent studies on fatigue and residual stresses, ultrasound inspection for the chain crown should be further investigated. A new application of ultrasonic phased array full matrix capture is discussed in this paper for investigation of the chain crown. Due to the complex geometry (i.e. curved and limited access) of the chain crown, a surface mapping technique has been added to the presented full matrix capture technique. The inspection method presented in this study is suitable for both air and underwater chain links. A continuous water supply wedge was developed in order to supply couplant for in air inspection. Development of a technique which can be adapted for robotic inspection is considered, and an automated manipulator was used to carry out inspections. The design of the inspection method and the robotic manipulator has been discussed in this article. The technique is validated with laboratory experiment