Monitoring of complex systems of interacting dynamic systems

Increases in functionality, power and intelligence of modern engineered systems led to complex systems with a large number of interconnected dynamic subsystems. In such machines, faults in one subsystem can cascade and affect the behavior of numerous other subsystems. This complicates the traditional fault monitoring procedures because of the need to train models of the faults that the monitoring system needs to detect and recognize. Unavoidable design defects, quality variations and different usage patterns make it infeasible to foresee all possible faults, resulting in limited diagnostic coverage that can only deal with previously anticipated and modeled failures. This leads to missed detections and costly blind swapping of acceptable components because of one’s inability to accurately isolate the source of previously unseen anomalies. To circumvent these difficulties, a new paradigm for diagnostic systems is proposed and discussed in this paper. Its feasibility is demonstrated through application examples in automotive engine diagnostics.

Characterisation of combustion related acoustic emission sources for diesel engine condition monitoring

This thesis represents a major step forward in understanding the link between the development of combustion related faults in diesel engines and the generation of acoustic emissions. The findings presented throughout the thesis provide a foundation so that future diesel engine monitoring systems are able to more effectively detect and monitor developing faults. In undertaking this research knowledge concerning engine function and relevant failure mechanisms was combined with different modelling methods to generate a framework that was used to effectively identify fault related activity within acoustic emissions recorded from different engines.

Fault detection in wind turbine : a systematic literature review

Wind power has become one of the popular renewable resources all over the world and is anticipated to occupy 12% of the total global electricity generation capacity by 2020. For the harsh environment that the wind turbine operates, fault diagnostic and condition monitoring are important for wind turbine safety and reliability. This paper employs a systematic literature review to report the most recent promotions in the wind turbine fault diagnostic, from 2005 to 2012. The frequent faults and failures in wind turbines are considered and different techniques which have been used by researchers are introduced, classified and discussed.

Precedent-free fault isolation in a diesel engine EGR valve system

In this paper, a framework for isolating unprecedented faults for an EGR valve system is presented. Using normal behavior data generated by a high fidelity engine simulation, the recently introduced Growing Structure Multiple Model System (GSMMS) is used to construct models of normal behavior for an EGR valve system and its various subsystems. Using the GSMMS models as a foundation, anomalous behavior of the entire system is then detected as statistically significant departures of the most recent modeling residuals from the modeling residuals during normal behavior. By reconnecting anomaly detectors to the constituent subsystems, the anomaly can be isolated without the need for prior training using faulty data. Furthermore, faults that were previously encountered (and modeled) are recognized using the same approach as the anomaly detectors.

Precedent-free fault isolation in a diesel engine exhaust gas recirculation system

In this paper, a recently introduced model-based method for precedent-free fault detection and isolation (FDI) is modified to deal with multiple input, multiple output (MIMO) systems and is applied to an automotive engine with exhaust gas recirculation (EGR) system. Using normal behavior data generated by a high fidelity engine simulation, the growing structure multiple model system (GSMMS) approach is used to construct dynamic models of normal behavior for the EGR system and its constituent subsystems. Using the GSMMS models as a foundation, anomalous behavior is detected whenever statistically significant departures of the most recent modeling residuals away from the modeling residuals displayed during normal behavior are observed. By reconnecting the anomaly detectors (ADs) to the constituent subsystems, EGR valve, cooler, and valve controller faults are isolated without the need for prior training using data corresponding to particular faulty system behaviors.

Tensile properties of Si nanowires with faulted stacking layers

Semiconductor nanowires (NWs) show tremendous applications in micro/nano-electro-mechanical systems. In order to fulfill their promising applications, an understanding of the mechanical properties of NWs becomes increasingly important. Based on the large-scale molecular dynamics simulations, this work investigated the tensile properties of Si NWs with different faulted stacking layers. Different faulted stacking layers were introduced around the centre of the NW by the insertion or removal of certain stacking layers, inducing twins, intrinsic stacking fault, extrinsic stacking fault, and 9R crystal structure. Stress–strain curves obtained from the tensile deformation tests reveal that the presence of faulted stacking layers has induced a considerable decrease to the yield strength while only a minor decrease to Young's modulus. The brittle fracture phenomenon is observed for all tested NWs. In particular, the formation of a monatomic chain is observed for the perfect NW, which exists for a relatively wide strain range. For the defected NW, the monatomic chain appears and lasts shorter. Additionally, all defected NWs show a fracture area near the two ends, in contrast to the perfect NW whose fracture area is adjacent to the middle. This study provides a better understanding of the mechanical properties of Si NWs with the presence of different faulted stacking layers.

What can we learn from the crashes of learner riders?

In some parts of Australia, people wanting to learn to ride a motorcycle are required to complete an off-road training course before they are allowed to practice on the road. In the state of Queensland, they are only required to pass a short multiple-choice road rules knowledge test. This paper describes an analysis of police-reported crashes involving learner riders in Queensland that was undertaken as part of research investigating whether pre-learner training is needed and, if so, the issues that should be addressed in training.. The crashes of learner riders and other riders were compared to identify whether there are particular situations or locations in which learner motorcyclists are over-involved in crashes, which could then be targeted in the pre-learner package. The analyses were undertaken separately for riders aged under 25 (330 crashes) versus those aged 25 and over (237 crashes) to provide some insight into whether age or riding inexperience are the more important factors, and thus to indicate whether there are merits in having different licensing or training approaches for younger and older learner riders. Given that the average age of learner riders was 33 years, under 25 was chosen to provide a sufficiently large sample of younger riders. Learner riders appeared to be involved in more severe crashes and to be more often at fault than fully-licensed riders but this may reflect problems in reporting, rather than real differences. Compared to open licence holders, both younger and older learner riders had relatively more crashes in low speed zones and relatively fewer in high speed zones. Riders aged under 25 had elevated percentages of night-time crashes and fewer single unit (potentially involving rider error only) crashes regardless of the type of licence held. The contributing factors that were more prevalent in crashes of learner riders than holders of open licences were: inexperience (37.2% versus 0.5%), inattention (21.5% versus 15.6%), alcohol or drugs (12.0% versus 5.1%) and drink riding (9.9% versus 3.1%). The pattern of contributing factors was generally similar for younger and older learner riders, although younger learners were (not surprisingly) more likely to have inexperience coded as a contributing factor (49.7% versus 19.8%). Some of the differences in crashes between learner riders and fully-licensed riders appear to reflect relatively more riding in urban areas by learners, rather than increased risks relating to inexperience. The analysis of contributing factors in learner rider crashes suggests that hazard perception and risk management (in terms of speed and alcohol and drugs) should be included in a pre-learner program. Currently, most learner riders in Queensland complete pre-licence training and become licensed within one month of obtaining their learner permit. If the introduction of pre-learner training required that the learner permit was held for a minimum duration, then the immediate effect might be more learners riding (and crashing). Thus, it is important to consider how training and licensing initiatives work together in order to improve the safety of new riders (and how this can be evaluated).

A qualitative investigation of drug use among Pakistani road users

Background Statistics on drug use by Pakistani drivers are not available, yet considerable numbers of drivers are believed to be drug addicted. The National Drug Abuse Assessment 2006/07, conducted by the United Nation Office on Drugs and Crime and the Ministry of Narcotics Control Pakistan reported that opiate users numbered 628,000, of which 77%were chronic heroin abusers. Injecting drug users have reportedly doubled in the decade to 2006 and drug use has been linked with many major crashes involving professional drivers. Aims This study explored a broad range of risk taking behaviours of road users, including drug use. It also investigated associations between risky road use and fatalism and other cultural beliefs. Methods This paper reports findings relating to drug driving in the cities of Lahore, Rawalpindi and Islamabad. Thirty semi-structured interviews were conducted with bus, truck, and taxi drivers, policy makers and field police officers. Results Interviews suggested widespread use of illicit drugs, particularly among bus, truck and taxi drivers. Reasons for drug use included recreational purposes, stimulants during long driving episodes, and substance addiction. Furthermore, the use of drugs and any association with road crashes was generally viewed as linked to fatalism rather than to any fault of an individual. In other words, people did not believe there was an association between drug use and road crashes, even if they had personally experienced such. Police knowledge of drug use among drivers was evident, although there is no formal drug driving testing regime in Pakistan. Discussion and conclusions The substantial increase in drug use among the population in recent years highlights a significant public health challenge in Pakistan. This qualitative research, although recognized as not representative of the broader population, suggests that there is significant cause for concern about drug driving, especially among professional drivers, and a need for further investigation and intervention.

Geologic map of the Valley Mountain 15’ quadrangle, San Bernardino and Riverside Counties, California: U.S. Geological Survey Geologic Quadrangle Map GQ–1767, scale 1:62,500

The Valley Mountain 15’ quadrangle straddles the Pinto Mountain Fault, which bounds the eastern Transverse Ranges in the south against the Mojave Desert province in the north. The Pinto Mountains, part of the eastern Transverse Ranges in the south part of the quadrangle expose a series of Paleoproterozoic gneisses and granite and the Proterozoic quartzite of Pinto Mountain. Early Triassic quartz monzonite intruded the gneisses and was ductiley deformed prior to voluminous Jurassic intrusion of diorite, granodiorite, quartz monzonite, and granite plutons. The Jurassic rocks include part of the Bullion Mountains Intrusive Suite, which crops out prominently at Valley Mountain and in the Bullion Mountains, as well as in the Pinto Mountains. Jurassic plutons in the southwest part of the quadrangle are deeply denuded from midcrustal emplacement levels in contrast to supracrustal Jurassic limestone and volcanic rocks exposed in the northeast. Dikes inferred to be part of the Jurassic Independence Dike Swarm intrude the Jurassic plutons and Proterozoic rocks. Late Cretaceous intrusion of the Cadiz Valley Batholith in the northeast caused contact metamorphism of adjacent Jurassic plutonic rocks...

Role of electromechanical wave propagation in power systems

In a large interconnected power system, disturbances initiated by a fault or other events cause acceleration in the generator rotors with respect to their synchronous reference frame. This acceleration of rotors can be described by two different dynamic phenomena, as shown in existing literature. One of the phenomena is simultaneous acceleration and the other is electromechanical wave propagation, which is characterized by travelling waves in terms of a wave equation. This paper demonstrates that depending on the structure of the system, the exhibited dynamic response will be dominated by one phenomenon or the other or a mixture of both. Two system structures of choice are examined, with each structure exemplifying each phenomenon present to different degrees in their dynamic responses. Prediction of dominance of either dynamic phenomenon in a particular system can be determined by taking into account the relative sizes of the values of its reduced admittance matrix.

The velocity synchronous discrete Fourier transform for order tracking in the field of rotating machinery

Diagnostics of rotating machinery has developed significantly in the last decades, and industrial applications are spreading in different sectors. Most applications are characterized by varying velocities of the shaft and in many cases transients are the most critical to monitor. In these variable speed conditions, fault symptoms are clearer in the angular/order domains than in the common time/frequency ones. In the past, this issue was often solved by synchronously sampling data by means of phase locked circuits governing the acquisition; however, thanks to the spread of cheap and powerful microprocessors, this procedure is nowadays rarer; sampling is usually performed at constant time intervals, and the conversion to the order domain is made by means of digital signal processing techniques. In the last decades different algorithms have been proposed for the extraction of an order spectrum from a signal sampled asynchronously with respect to the shaft rotational velocity; many of them (the so called computed order tracking family) use interpolation techniques to resample the signal at constant angular increments, followed by a common discrete Fourier transform to shift from the angular domain to the order domain. A less exploited family of techniques shifts directly from the time domain to the order spectrum, by means of modified Fourier transforms. This paper proposes a new transform, named velocity synchronous discrete Fourier transform, which takes advantage of the instantaneous velocity to improve the quality of its result, reaching performances that can challenge the computed order tracking.

Development of a new signal processing diagnostic tool for vibration signals acquired in transient conditions

The diagnostics of mechanical components operating in transient conditions is still an open issue, in both research and industrial field. Indeed, the signal processing techniques developed to analyse stationary data are not applicable or are affected by a loss of effectiveness when applied to signal acquired in transient conditions. In this paper, a suitable and original signal processing tool (named EEMED), which can be used for mechanical component diagnostics in whatever operating condition and noise level, is developed exploiting some data-adaptive techniques such as Empirical Mode Decomposition (EMD), Minimum Entropy Deconvolution (MED) and the analytical approach of the Hilbert transform. The proposed tool is able to supply diagnostic information on the basis of experimental vibrations measured in transient conditions. The tool has been originally developed in order to detect localized faults on bearings installed in high speed train traction equipments and it is more effective to detect a fault in non-stationary conditions than signal processing tools based on spectral kurtosis or envelope analysis, which represent until now the landmark for bearings diagnostics.

Dynamic effects of electrical pitting in steam-turbine tilting-pad thrust-bearings

This paper merges the analysis of a case history and the simplified theoretical model related to a rather singular phenomenon that may happen in rotating machinery. Starting from the first, a small industrial steam turbine experienced a very strange behavior during megawatt load. When the unit was approaching the maximum allowed power, the temperature of the babbitt metal of the pads of the thrust bearing showed constant increase with an unrecoverable drift. Bearing inspection showed that pad trailing edge had the typical aspect of electrical pitting. This kind of damage was not reparable and bearing pads had to replaced. This problem occurred several times in sequence and was solved only by adding further ground brushes to the shaft-line. Failure analysis indicated electrodischarge machining as the root fault. A specific model, able to take into consideration the effect of electrical pitting and loading capacity decreasing as a consequence of the damage of the babbitt metal, is proposed in the paper and shows that the phenomenon causes the irretrievable failure of the thrust bearing.

The combination of empirical mode decomposition and minimum entropy deconvolution for roller bearing diagnostics in non-stationary operation

Diagnostics is based on the characterization of mechanical system condition and allows early detection of a possible fault. Signal processing is an approach widely used in diagnostics, since it allows directly characterizing the state of the system. Several types of advanced signal processing techniques have been proposed in the last decades and added to more conventional ones. Seldom, these techniques are able to consider non-stationary operations. Diagnostics of roller bearings is not an exception of this framework. In this paper, a new vibration signal processing tool, able to perform roller bearing diagnostics in whatever working condition and noise level, is developed on the basis of two data-adaptive techniques as Empirical Mode Decomposition (EMD), Minimum Entropy Deconvolution (MED), coupled by means of the mathematics related to the Hilbert transform. The effectiveness of the new signal processing tool is proven by means of experimental data measured in a test-rig that employs high power industrial size components.

Bearing Fault Diagnostics Using the Spectral Pattern Recognition

In the field of diagnostics of rolling element bearings, the development of sophisticated techniques, such as Spectral Kurtosis and 2nd Order Cyclostationarity, extended the capability of expert users to identify not only the presence, but also the location of the damage in the bearing. Most of the signal-analysis methods, as the ones previously mentioned, result in a spectrum-like diagram that presents line frequencies or peaks in the neighbourhood of some theoretical characteristic frequencies, in case of damage. These frequencies depend only on damage position, bearing geometry and rotational speed. The major improvement in this field would be the development of algorithms with high degree of automation. This paper aims at this important objective, by discussing for the first time how these peaks can draw away from the theoretical expected frequencies as a function of different working conditions, i.e. speed, torque and lubrication. After providing a brief description of the peak-patterns associated with each type of damage, this paper shows the typical magnitudes of the deviations from the theoretical expected frequencies. The last part of the study presents some remarks about increasing the reliability of the automatic algorithm. The research is based on experimental data obtained by using artificially damaged bearings installed in a gearbox.