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The detection and diagnosis of faults in industrial processes is a very active field of research due to the reduction in maintenance costs achieved by the implementation of process monitoring algorithms such as Principal Component Analysis, Partial Least Squares or more recently Canonical Variate Analysis (CVA). Typically the condition of rotating machinery is monitored separately using vibration analysis or other specific techniques. Conventional vibration-based condition monitoring techniques are based on the tracking of key features observed in the measured signal. Typically steady-state loading conditions are required to ensure consistency between measurements. In this paper, a technique based on merging process and vibration data is proposed with the objective of improving the detection of mechanical faults in industrial systems working under variable operating conditions. The capabilities of CVA for detection and diagnosis of faults were tested using experimental data acquired from a compressor test rig where different process faults were introduced. Results suggest that the combination of process and vibration data can effectively improve the detectability of mechanical faults in systems working under variable operating conditions.

One of the major targets in industry is minimisation of downtime and cost, and maximisation of availability and safety, with maintenance considered a key aspect in achieving this objective. The concept of Condition Based Maintenance and Prognostics and Health Management (CBM/PHM) , which is founded on the principles of diagnostics, and prognostics, is a step towards this direction as it offers a proactive means for scheduling maintenance. Reciprocating compressors are vital components in oil and gas industry, though their maintenance cost is known to be relatively high. Compressor valves are the weakest part, being the most frequent failing component, accounting for almost half maintenance cost. To date, there has been limited information on estimating Remaining Useful Life (RUL) of reciprocating compressor in the open literature. This paper compares the prognostic performance of several methods (multiple linear regression, polynomial regression, Self-Organising Map (SOM), K-Nearest Neighbours Regression (KNNR)), in relation to their accuracy and precision, using actual valve failure data captured from an operating industrial compressor. The SOM technique is employed for the first time as a standalone tool for RUL estimation. Furthermore, two variations on estimating RUL based on SOM and KNNR respectively are proposed. Finally, an ensemble method by combining the output of all aforementioned algorithms is proposed and tested. Principal components analysis and statistical process control were implemented to create T^2 and Q metrics, which were proposed to be used as health indicators reflecting degradation processes and were employed for direct RUL estimation for the first time. It was shown that even when RUL is relatively short due to instantaneous nature of failure mode, it is feasible to perform good RUL estimates using the proposed techniques.

Helicopters are extensively used in civil applications as they are versatile in their capabilities to manoeuvre. Their operation under harsh conditions and environments demand for a strict maintenance plan. Main gearboxes (MGB) of helicopters are a critical component responsible for reducing the high input speed generated from the gas turbine engines. Health and Usage Monitoring Systems (HUMS) are installed in an effort to monitor the health state of the transmission systems, and ideally, to detect and diagnose the type of a generated fault. Even though the development of HUMS contributed to the reduction of worldwide helicopter accident rate, more advanced systems are needed based on the investigation of the air accidents of AS332 L2 Super Puma in Scotland in 2009 and of EC225 LP Super Puma in Bergen in 2016, due to failure of a planet gear of the MGB. A plethora of signal processing methodologies have been proposed for the early detection of faults but often they fail in complex structures, such as planetary gearboxes operating under various conditions. In this paper the performance of a recently proposed diagnostic tool, called IESFOgram, is evaluated and compared with state of the art techniques, applied on signal captured on a Category A Super Puma SA330 MGB.

The application of high-frequency acoustic emission (AE) technology to condition monitoring of gears is still in its infancy. Understanding the influence of gear operating parameters on the generation of AE is essential in applying the AE technology to gear condition monitoring. This paper presents experimental findings on the influence of speed and load in generating AE for operating helical and spur gears. The experimental findings suggest that any percentage reduction in specific film thickness (gimel), a direct consequence of a change in load condition, results in a nine- and four-fold percentage change in AE rms for the spur and helical gear sets, respectively. A numerical model representing changes in AE rms with variation in load and speed under near isothermal conditions for spur and helical gears was also established. In conclusion, it is postulated that the AE technology could offer a means of measuring in situ the effectiveness of a lubricant for operational spur and helical gears thereby establishing if the correct lubricating conditions are present to ensure optimal life usage.

In this paper, two new Mindlin-type plate bending elements have been derived for the modelling of functionally graded plate subjected to various loading conditions such as tensile loading, in-plane bending and out-of-plane bending. The properties of the first Mindlin-type element (i.e. Average Mindlin element) are computed by using an average fibre distribution technique which averages the macro-mechanical properties over each element. The properties of the second Mindlin-type element (i.e. Smooth Mindlin element) are computed by using a smooth fibre distribution technique, which directly uses the macro-mechanical properties at Gaussian quadrature points of each element. There were two types of non-linearity considered in the modelling of the plate, which include finite strain and material degradation. The composite plate considered in this paper is functionally graded in the longitudinal direction only, but the FE code developed is capable of analysing composite plates with functional gradation in transverse and radial direction as well. This study was able to show that the structural integrity enhancement and strength maximisation of composite structures are achievable through functional gradation of material properties over the structure.

This paper presents results of an experimental programme that suggested a correlation between levels of Acoustic Emission (AE) activity and the frictional resistance of contacting asperities of spur gears under partial elastohydrodynamic lubrication.

Acoustic emission (AE) measurement at the bearings of rotating machinery has become a useful tool for diagnosing incipient fault conditions. In particular, AE can be used to detect unwanted intermittent or partial rubbing between a rotating central shaft and surrounding stationary components. This is a particular problem encountered in gas turbines used for power generation. For successful fault diagnosis, it is important to adopt AE signal analysis techniques capable of distinguishing between various types of rub mechanisms. It is also useful to develop techniques for inferring information such as the severity of rubbing or the type of seal material making contact on the shaft. It is proposed that modelling the cumulative distribution function of rub-induced AE signals with respect to appropriate theoretical distributions, and quantifying the goodness of fit with the Kolmogorov-Smirnov (KS) statistic, offer a suitable signal feature for diagnosis. This paper demonstrates the successful use of the KS feature for discriminating different classes of shaft- seal rubbing. A hierarchical cluster algorithm was employed for grouping extracted KS values. AE rub signals were simulated with various metallic seals and measured at the journal bearings of a test rig rotating at approximately 1500 rev/min. Also, the KS classification results were directly compared withmore established AE feature vectors.