Integrated health monitoring is beneficial but due to reliability, weight, size, wiring and other constraints, the incorporation of instrumentation onto aircraft propulsion systems is limited. Conventional wired sensing systems are not always feasible due to size, weight constraints, and issues associated with cable routing. This paper presents an integrated and selfpowered wireless system for high temperature (above 125 • C) environments powered by a thermoelectric generator for bearing condition monitoring. Thermoelectric generator with internal oil cooling chamber is proposed to achieve higher energy output for small temperature gradient recorded in the jet engine in comparison with other thermoelectric generators with heat sinks. The experimental results demonstrate that, under a simulated engine environment, the thermoelectric generator can provide sufficient energy for a wireless sensing system to collect environmental data every 46 s, and transmit every 260 s, during the critical takeoff phase of flight and part of cruise.

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Additive manufacturing methods are becoming more and more important due to improvements in the technologies used for “printing,” although additive manufacturing remains a very expensive technology. As such, additive manufacturing is predominantly used in aerospace or medical applications for complex parts that cannot be manufactured with conventional methods. M50NiL, a carburizing heat-resistant steel, is typically used for main shaft bearings in aerospace gas turbine engines. A selective laser melting process for M50NiL was developed for such applications. Analysis of microstructure, hardness, and tensile and rolling contact fatigue testing demonstrated equivalence of M50NiL AM components to components of conventional M50NiL. This equivalence enables new degrees of freedom in designs for main engine shaft bearings (e.g., the integration of oil cooling channels in the outer ring). A comparison of theoretical optimized cooling channel designs and those possible via additive manufacturing was performed considering geometry, surface roughness, and location of the cooling channels.

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Intelligent fault classification of rolling element bearings (REBs) using machine learning (ML) techniques increases the reliability of industrial assets. One of the main issues associated with ML model development is the lack of training data and, most importantly, the ability of models to be used for applications without specific training data, ie the generalisation capability of models. This study investigates the feasibility of using multinomial logistic regression (MLR) as generalised ML models for rolling element bearing fault classification without the requirement of training data for new bearing designs and varied machine operations. This has been achieved by using bearing characteristic frequencies (BCFs) as inputs to the MLR models extracted by a newly developed hybrid method. The new method combines cepstrum pre-whitening (CPW) and full-band enveloping, which can effectively identify the BCFs in vibration data from various machines. This paper presents the methods of the feature extraction and the development of generalised ML models for REBs based on data from the EU Clean Sky 2 I2BS project1. This model is then validated by data from Case Western Reserve University (CWRU) and the Society for Machinery Failure Prevention Technology (MFPT), available in the public domain without further training.