Development of a diagnostic tool for condition monitoring of rotating machinery

This paper presents an overview of the CRC for Infrastructure and Engineering Asset Management (CIEAM)’s rotating machine health monitoring project and the status of the research progress. The project focuses on the development of a comprehensive diagnostic tool for condition monitoring and systematic analysis of rotating machinery. Particularly attention focuses on the machine health monitoring of diesel engines, compressors and pumps by using acoustic emission and vibration-based monitoring techniques. The paper also provides a brief summary of the work done by the three main research collaborating partners in the project, namely, Queensland University of Technology (QUT), Curtin University of Technology (CUT) and the University of Western Australia (UWA). Preliminary test and analysis results from this work are also reported in the paper

A practical signal processing approach for condition monitoring of low speed machinery using Peak-Hold-Down-Sample algorithm

A simple and effective down-sample algorithm, Peak-Hold-Down-Sample (PHDS) algorithm is developed in this paper to enable a rapid and efficient data transfer in remote condition monitoring applications. The algorithm is particularly useful for high frequency Condition Monitoring (CM) techniques, and for low speed machine applications since the combination of the high sampling frequency and low rotating speed will generally lead to large unwieldy data size. The effectiveness of the algorithm was evaluated and tested on four sets of data in the study. One set of the data was extracted from the condition monitoring signal of a practical industry application. Another set of data was acquired from a low speed machine test rig in the laboratory. The other two sets of data were computer simulated bearing defect signals having either a single or multiple bearing defects. The results disclose that the PHDS algorithm can substantially reduce the size of data while preserving the critical bearing defect information for all the data sets used in this work even when a large down-sample ratio was used (i.e., 500 times down-sampled). In contrast, the down-sample process using existing normal down-sample technique in signal processing eliminates the useful and critical information such as bearing defect frequencies in a signal when the same down-sample ratio was employed. Noise and artificial frequency components were also induced by the normal down-sample technique, thus limits its usefulness for machine condition monitoring applications.

Fault detection of slow speed rolling element bearing with noise removal techniques

Rolling Element Bearings (REBs) are vital components in rotating machineries for providing rotating motion. In slow speed rotating machines, bearings are normally subjected to heavy static loads and a catastrophic failure can cause enormous disruption to production and human safety. Due to its low operating speed the impact energy generated by the rotating elements on the defective components is not sufficient to produce a detectable vibration response. This is further aggravated by the inability of general measuring instruments to detect and process the weak signals at the initiation of the defect accurately. Furthermore, the weak signals are often corrupted by background noise. This is a serious problem faced by maintenance engineers today and the inability to detect an incipient failure of the machine can significantly increases the risk of functional failure and costly downtime. This paper presents the application of noise removal techniques for enhancing the detection capability for slow speed REB condition monitoring. Blind deconvolution (BD) and adaptive line enhancer (ALE) are compared to evaluate their performance in enhancing the source signal with consequential removal of background noise. In the experimental study, incipient defects were seeded on a number of roller bearings and the signals were acquired using acoustic emission (AE) sensor. Kurtosis and modified peak ratio (mPR) were used to determine the detectability of signal corrupted by noise.

Direct and quantitative detection of bacteriophage by “hearing” surface detachment using a quartz crystal microbalance

We show that it is possible to detect specifically adsorbed bacteriophage directly by breaking the interactions between proteins displayed on the phage coat and ligands immobilized on the surface of a quartz crystal microbalance (QCM). This is achieved through increasing the amplitude of oscillation of the QCM surface and sensitively detecting the acoustic emission produced when the bacteriophage detaches from the surface. There is no interference from nonspecifically adsorbed phage. The detection is quantitative over at least 5 orders of magnitude and is sensitive enough to detect as few as 20 phage. The method has potential as a sensitive and low-cost method for virus detection.

Direct and quantitative detection of bacteriophage by "hearing" surface detachment using a quartz crystal microbalance

We show that it is possible to detect specifically adsorbed bacteriophage directly by breaking the interactions between proteins displayed on the phage coat and ligands immobilized on the surface of a quartz crystal microbalance (QCM). This is achieved through increasing the amplitude of oscillation of the QCM surface and sensitively detecting the acoustic emission produced when the bacteriophage detaches from the surface. There is no interference from nonspecifically adsorbed phage. The detection is quantitative over at least 5 orders of magnitude and is sensitive enough to detect as few as 20 phage. The method has potential as a sensitive and low-cost method for virus detection.

Condition monitoring and fault diagnosis of diesel engines using instantaneous angular speed analysis

Continuous monitoring of diesel engine performance is critical for early detection of fault developments in an engine before they materialize into a functional failure. Instantaneous crank angular speed (IAS) analysis is one of a few nonintrusive condition monitoring techniques that can be utilized for such a task. Furthermore, the technique is more suitable for mass industry deployments than other non-intrusive methods such as vibration and acoustic emission techniques due to the low instrumentation cost, smaller data size and robust signal clarity since IAS is not affected by the engine operation noise and noise from the surrounding environment. A combination of IAS and order analysis was employed in this experimental study and the major order component of the IAS spectrum was used for engine loading estimation and fault diagnosis of a four-stroke four-cylinder diesel engine. It was shown that IAS analysis can provide useful information about engine speed variation caused by changing piston momentum and crankshaft acceleration during the engine combustion process. It was also found that the major order component of the IAS spectra directly associated with the engine firing frequency (at twice the mean shaft rotating speed) can be utilized to estimate engine loading condition regardless of whether the engine is operating at healthy condition or with faults. The amplitude of this order component follows a distinctive exponential curve as the loading condition changes. A mathematical relationship was then established in the paper to estimate the engine power output based on the amplitude of this order component of the IAS spectrum. It was further illustrated that IAS technique can be employed for the detection of a simulated exhaust valve fault in this study.

Reliable Fault Diagnosis for Low-Speed Bearings Using Individually Trained Support Vector Machines With Kernel Discriminative Feature Analysis

This paper proposes a highly reliable fault diagnosis approach for low-speed bearings. The proposed approach first extracts wavelet-based fault features that represent diverse symptoms of multiple low-speed bearing defects. The most useful fault features for diagnosis are then selected by utilizing a genetic algorithm (GA)-based kernel discriminative feature analysis cooperating with one-against-all multicategory support vector machines (OAA MCSVMs). Finally, each support vector machine is individually trained with its own feature vector that includes the most discriminative fault features, offering the highest classification performance. In this study, the effectiveness of the proposed GA-based kernel discriminative feature analysis and the classification ability of individually trained OAA MCSVMs are addressed in terms of average classification accuracy. In addition, the proposedGA- based kernel discriminative feature analysis is compared with four other state-of-the-art feature analysis approaches. Experimental results indicate that the proposed approach is superior to other feature analysis methodologies, yielding an average classification accuracy of 98.06% and 94.49% under rotational speeds of 50 revolutions-per-minute (RPM) and 80 RPM, respectively. Furthermore, the individually trained MCSVMs with their own optimal fault features based on the proposed GA-based kernel discriminative feature analysis outperform the standard OAA MCSVMs, showing an average accuracy of 98.66% and 95.01% for bearings under rotational speeds of 50 RPM and 80 RPM, respectively.

Reliable fault diagnosis for incipient low-speed bearings using fault feature analysis based on a binary bat algorithm

In this paper, we propose a highly reliable fault diagnosis scheme for incipient low-speed rolling element bearing failures. The scheme consists of fault feature calculation, discriminative fault feature analysis, and fault classification. The proposed approach first computes wavelet-based fault features, including the respective relative wavelet packet node energy and entropy, by applying a wavelet packet transform to an incoming acoustic emission signal. The most discriminative fault features are then filtered from the originally produced feature vector by using discriminative fault feature analysis based on a binary bat algorithm (BBA). Finally, the proposed approach employs one-against-all multiclass support vector machines to identify multiple low-speed rolling element bearing defects. This study compares the proposed BBA-based dimensionality reduction scheme with four other dimensionality reduction methodologies in terms of classification performance. Experimental results show that the proposed methodology is superior to other dimensionality reduction approaches, yielding an average classification accuracy of 94.9%, 95.8%, and 98.4% under bearing rotational speeds at 20 revolutions-per-minute (RPM), 80 RPM, and 140 RPM, respectively.

Comparison of the sensitivity of different sensor technologies to imbalance severity in low speed wind turbines

Imbalance is not only a direct major cause of downtime in wind turbines, but also accelerates the degradation of neighbouring and downstream components (e.g. main bearing, generator). Along with detection, the imbalance quantification is also essential as some residual imbalance always exist even in a healthy turbine. Three different commonly used sensor technologies (vibration, acoustic emission and electrical measurements) are investigated in this work to verify their sensitivity to different imbalance grades. This study is based on data obtained by experimental tests performed on a small scale wind turbine drive train test-rig for different shaft speeds and imbalance levels. According to the analysis results, electrical measurements seem to be the most suitable for tracking the development of imbalance.

Testing the effects of the Japanese vehicle emission-control law on the international palladium futures market

This paper tested the effects of the 2005 vehicle emission-control law issued in Japan on the market linkages between the U.S. and Japanese palladium futures markets, To determine these effects, we applied a cointegration test both with and without break points in the time series and found that the market linkages between the two countries changed after the break in October 2005. Our results show that the 2005 long-term regulation of vehicle emissions enacted in Japan influenced the international palladium futures market.

Modeling nitrous oxide emissions from irrigated agriculture : testing DayCent with high-frequency measurements

A unique high temporal frequency dataset from an irrigated cotton-wheat rotation was used to test the agroecosystem model DayCent to simulate daily N2O emissions from sub-tropical vertisols under different irrigation intensities. DayCent was able to simulate the effect of different irrigation intensities on N2O fluxes and yield, although it tended to overestimate seasonal fluxes during the cotton season. DayCent accurately predicted soil moisture dynamics and the timing and magnitude of high fluxes associated with fertilizer additions and irrigation events. At the daily scale we found a good correlation of predicted vs. measured N2O fluxes (r2 = 0.52), confirming that DayCent can be used to test agricultural practices for mitigating N2O emission from irrigated cropping systems. A 25 year scenario analysis indicated that N2O losses from irrigated cotton-wheat rotations on black vertisols in Australia can be substantially reduced by an optimized fertilizer and irrigation management system (i.e. frequent irrigation, avoidance of excessive fertiliser application), while sustaining maximum yield potentials.

Relating urban airborne particle concentrations to shipping using carbon based elemental emission ratios

This study demonstrates a novel method for testing the hypothesis that variations in primary and secondary particle number concentration (PNC) in urban air are related to residual fuel oil combustion at a coastal port lying 30 km upwind, by examining the correlation between PNC and airborne particle composition signatures chosen for their sensitivity to the elemental contaminants present in residual fuel oil. Residual fuel oil combustion indicators were chosen by comparing the sensitivity of a range of concentration ratios to airborne emissions originating from the port. The most responsive were combinations of vanadium and sulfur concentration ([S], [V]) expressed as ratios with respect to black carbon concentration ([BC]). These correlated significantly with ship activity at the port and with the fraction of time during which the wind blew from the port. The average [V] when the wind was predominantly from the port was 0.52 ng.m-3 (87%) higher than the average for all wind directions and 0.83 ng.m-3 (280%) higher than that for the lowest vanadium yielding wind direction considered to approximate the natural background. Shipping was found to be the main source of V impacting urban air quality in Brisbane. However, contrary to the stated hypothesis, increases in PNC related measures did not correlate with ship emission indicators or ship traffic. Hence at this site ship emissions were not found to be a major contributor to PNC compared to other fossil fuel combustion sources such as road traffic, airport and refinery emissions.

Thermal Treatments of Fe—Mn Alkoxide of Glycerol: Infrared absorption and emission

Synthetic Fe—Mn alkoxide of glycerol samples are submitted to controlled heating conditions and examined by IR absorption spectroscopy. On the other hand, the same sample is studied by infrared emission spectroscopy (IRES), upon heating in situ from 100 to 600°C. The spectral techniques employed in this contribution, especially IRES, show that as a result of the thermal treatments ferromagnetic oxides (manganese ferrite) are formed between 350 and 400°C. Some further spectral changes are seen at higher temperatures.