"Selection of Input Parameters for Multivariate Classifiersin Proactive Machine Health Monitoring by Clustering Envelope Spectrum Harmonics"

In condition monitoring (CM) signal analysis the inherent problem of key characteristics being masked by noise can be addressed by analysis of the signal envelope. Envelope analysis of vibration signals is effective in extracting useful information for diagnosing different faults. However, the number of envelope features is generally too large to be effectively incorporated in system models. In this paper a novel method of extracting the pertinent information from such signals based on multivariate statistical techniques is developed which substantialy reduces the number of input parameters required for data classification models. This was achieved by clustering possible model variables into a number of homogeneous groups to assertain levels of interdependency. Representatives from each of the groups were selected for their power to discriminate between the categorical classes. The techniques established were applied to a reciprocating compressor rig wherein the target was identifying machine states with respect to operational health through comparison of signal outputs for healthy and faulty systems. The technique allowed near perfect fault classification. In addition methods for identifying seperable classes are investigated through profiling techniques, illustrated using Andrew’s Fourier curves

Behavior of a CI Engine Running by Biodiesel under Transient Conditions

The emission characteristics of compression ignition (CI) engines running on biodiesel during transient operating conditions, which is the most usual case in urban and extra-urban transportation, have rarely been investigated. In the present study an experimental investigation on emission characteristics of a CI engine has been carried out both under steady state and transient operating conditions. The experimental work has been carried out on CI engine, which is integrated with transient testing facility. This facility is capable of varying the engine speed and load over a given time period. To measure the engine emissions, an emission analyser has been used to measure CO2, CO, THC, and NOx emissions. The fuels used in the analyses are 25% (25B) and 100% (100B) of biodiesel blend and diesel. The series of the transient events studied are speed changes from 900 to 1200rpm, 1200 to 1500rpm and 1500 to 1800rpm over a time period of 4 seconds each. These tests were performed at a constant load of 105Nm, 210Nm, 315Nm and 420Nm. The transient test results have shown that the emissions of CI engine running on biodiesel were reduced by up to 17%, 52% and 38% for CO, CO2 and THC emissions respectively as compared to diesel fuel. However, the NOx emission was seen to be 17% higher for engine running on biodiesel than that on diesel during transient conditions

Investigation of a Rotating Shaft with a Novel Integrated Wireless Accelerometer

Rotating shafts are the most critical components of rotating machines such as motors, pumps, engines and turbines. Due to their heavy workloads, defects are more likely to develop during operation. There are many techniques used to monitor shaft defects by analysing the vibration of the shaft as well as the instantaneous angular speed (IAS) of the shaft. The signals are measured either using non-contact techniques such as laser-based measurement or indirect measurement such as the vibration on bearing housings. The advancement in low cost and low power Micro Electro Mechanical Systems (MEMS) make it possible to develop an integrated wireless sensor mounted on rotating shafts directly. This can make the fault diagnosis of rotating shafts more effective as it is likely to capture more details of shaft dynamics. This paper presents a novel integrated wireless accelerometer mounted directly on a rotating shaft and demonstrates that it can effectively monitor different degree of misalignments occurring commonly in a shaft system

Combustion heat release models of biodiesels

Fossil fuels such as standard gasoline and diesel fuel are the most important source of energy for our society today, providing the bulk of global energy requirements for transportation, construction, heating, and agriculture. Many new developments in technology have made alternative sources of energy more economically feasible including advances in solar, wind, geothermal and nuclear energy. It is a domestic, clean-burning, renewable liquid fuel that can be used in compression-ignition engines instead of petroleum-based diesel with little or no modifications. Biodiesel blends are more commonly used than pure B100 fuels. The main reason for this is that running 100% biodiesel sometimes requires modifications to the engine, due to the higher content of alcohol present in biodiesel

A review of mechanical seals tribology and condition monitoring

Mechanical seals have become one of the most popular sealing systems for rotating machinery because of low leakage and absence of a requirement for routine maintenance. Generally, a mechanical face seal should operate with a fluid film as thin as possible, to reduce the leakage and to restrict friction and wear. Recent advances in a system of computer software based on finite element modelling and analytical approaches help in understanding of the working conditions of the mechanical face seals. This paper reviews tribological bahavior and condition monitoring of mechanical seals based on the literature of the recent years. It covers friction, wear and thermal characteristics of mechanical seals and the application of computational methods and other techniques to give good understanding of the tribological behavior and condition monitoring of seal faces

Non-parametric models in the monitoring of engine performance and condition: Part 2: non-intrusive estimation of diesel engine cylinder pressure and its use in fault detection

An application of the radial basis function model, described in Part 1, is demonstrated on a four-cylinder DI diesel engine with data from a wide range of speed and load settings. The prediction capabilities of the trained model are validated against measured data and an example is given of the application of this model to the detection of a slight fault in one of the cylinders

Selection of Input Parameters for Multivariate Classifiers in Proactive Machine Health Monitoring by Clustering Envelope Spectrum Harmonics

In condition monitoring (CM) signal analysis the inherent problem of key characteristics being masked by noise can be addressed by analysis of the signal envelope. Envelope analysis of vibration signals is effective in extracting useful information for diagnosing different faults. However, the number of envelope features is generally too large to be effectively incorporated in system models. In this paper a novel method of extracting the pertinent information from such signals based on multivariate statistical techniques is developed which substantialy reduces the number of input parameters required for data classification models. This was achieved by clustering possible model variables into a number of homogeneous groups to assertain levels of interdependency. Representatives from each of the groups were selected for their power to discriminate between the categorical classes. The techniques established were applied to a reciprocating compressor rig wherein the target was identifying machine states with respect to operational health through comparison of signal outputs for healthy and faulty systems. The technique allowed near perfect fault classification. In addition methods for identifying seperable classes are investigated through profiling techniques, illustrated using Andrew’s Fourier curves

Observer-based Fault Detection and Diagnosis for Mechanical Transmission Systems with Sensorless Variable Speed Drives

Observer based approaches are commonly embedded in sensorless variable speed drives for the purpose of speed control. It estimates system variables to produce errors or residual signals in conjunction with corresponding measurements. The residual signals then are relied to tune control parameters to maintain operational performance even if there are considerable disturbances such as noises and component faults. Obviously, this control strategy outcomes robust control performances. However, it may produce adverse consequences to the system when faults progress to high severity. To prevent the occurrences of such consequences, this research proposes the utilisation of residual signals as detection features to raise alerts for incipient faults. Based on a gear transmission system with a sensorless variable speed drive (VSD), observers for speed, flux and torque are developed for examining their residuals under two mechanical faults: tooth breakage with different degrees of severities and shortage of lubricant at different levels are investigated. It shows that power residual signals can be based on to indicate different faults, showing that the observer based approaches are effective for monitoring VSD based mechanical systems. Moreover, it also shows that these two types fault can be separated based on the dynamic components in the voltage signals

Pitting damage levels estimation for planetary gear sets based on model simulation and grey relational analysis

The planetary gearbox is a critical mechanism in helicopter transmission systems. Tooth failures in planetary gear sets will cause great risk to helicopter operations. A gear pitting damage level estimation methodology has been devised in this paper by integrating a physical model for simulation signal generation, a three-step statistic algorithm for feature selection and damage level estimation for grey relational analysis. The proposed method was calibrated firstly with fault seeded test data and then validated with the data of other tests from a planetary gear set. The estimation results of test data coincide with the actual test records, showing the effectiveness and accuracy of the method in providing a novel way to model based methods and feature selection and weighting methods for more accurate health monitoring and condition prediction

Gear wear process monitoring using acoustic signals

Airborne acoustic signals contain valuable information from machines and can be detected remotely for condition monitoring. However, the signal is often seriously contaminated by various noises from the environment as well as nearby machines. This paper presents an acoustic based method of monitoring a two stage helical gearbox, a common power transmission system used in various industries. A single microphone is employed to measure the acoustics of the gearbox under-going a run-to-failure test. To suppress the background noise and interferences from nearby ma-chines a modulation signal bispectrum (MSB) analysis is applied to the signal. It is shown that the analysis allows the meshing frequency components and the associated shaft modulating components to be captured more accurately to set up a clear monitoring trend to indicate the tooth wear of the gears under test. The results demonstrate that acoustic signals in conjunction with efficient signal processing methods provide an effective monitoring of the gear transmission process