Mani Entezami

Enhanced fault diagnosis of roller bearing elements using a combination of empirical mode decomposition and minimum entropy deconvolution

This paper introduces a new signal processing algorithm for vibration-based fault detection and diagnosis of roller bearings. The methodology proposed in this paper is based on the combination of two data-adaptive techniques which are further enhanced through the use of an automatic feature identification mechanism. The new technique, introduced as empirical mode envelope with minimum entropy, combines elements from the empirical mode decomposition (EMD) and minimum entropy deconvolution (MED) approaches with an energy moment technique to improve the feature selection stage of the EMD algorithm. This improvement allows the processing chain to identify early stage roller bearing faults in noisier signals. The energy moment technique is used to automatically identify the most appropriate intrinsic mode function from the EMD process prior to the MED algorithm being applied. This is in contrast to conventional approaches which tend to use the first mode or make selections based on traditional energy techniques. The combination of the adaptive techniques of EMD and MED allows the development of an improved technique for fault detection and diagnosis of signals. Combining these techniques with the energy moment approach allows further improved fault detection in complex non-stationary conditions. The processing chain has been tested using data obtained during laboratory testing. From the experimental results, it is shown that the new technique is capable of the detection of early stage (minor) roller and outer race defects found in tapered-roller-bearings rotating at a variety of speeds and noise scenarios.

Development and design of a narrow-gauge hydrogen-hybrid locomotive

Hydrogen used as an energy carrier is a promising alternative to diesel for autonomous railway motive power, but, globally, few prototypes exist. In 2012, the Institution of Mechanical Engineers held the inaugural Railway Challenge, in which the participating teams had to develop, design and construct a locomotive to run on 10.25 inch (260.35 mm) gauge track while meeting certain set design criteria as well as competing in operational challenges. The University of Birmingham Railway Challenge Team’s locomotive design is described in this paper. The vehicle is the UK’s first hydrogen-powered locomotive and is called Hydrogen Pioneer. The drive-system consists of a hydrogen tank, a 1.1 kW proton-exchange-membrane fuel cell stack, a 4.3 kWh battery pack and two 2.2 kW permanent-magnet traction motors. The development of the locomotive, from the original concept to the final design, and the design validation are all presented in this paper. The locomotive completed successfully all challenges through which the proof of the concept of a hydrogen-hybrid locomotive was established.

Wayside detection of faults in railway axle bearings using time spectral kurtosis analysis on high-frequency acoustic emission signals:

Typical railway wheelsets consist of wheels, axle and axle bearings. Faults can develop on any of the aforementioned components, but the most common are related to wheel and axle bearing defects. The continuous increase in train operating speeds means that failure of an axle bearing can lead to serious derailments, causing loss of life and severe disruption in the operation of the network, damage to the track and loss of confidence in rail transport by the general public. The rail industry has focused on the improvement of maintenance and remote condition monitoring of rolling stock to reduce the probability of failure as much as realistically possible. Current wayside systems such as hot axle box detectors and acoustic arrays may fail to detect defective bearings. This article discusses the results of wayside high-frequency acoustic emission measurements performed on freight rolling stock with artificially induced damage in axle bearings in Long Marston, UK. Time spectral kurtosis is applied for the analysis of the acoustic emission data. From the results obtained, it is evident that time spectral kurtosis is capable of distinguishing the axle bearing defects from the random noises produced by different sources such as the wheel–rail interaction, braking and changes in train speed.