An approach to fault diagnosis for gearbox based on order tracking and extreme learning machine

Varying speed machinery condition detection and fault diagnosis are more difficult due to non-stationary machine dynamics and vibration. In this paper, an intelligent fault diagnosis method based on order analysis and extreme learning machine (ELM) is proposed. Order tracking, easily identifying speed-related vibrations, is useful for machine condition monitoring, which could obtain the resampling signal of constant increment angle. Then, the power spectrum (PS) of characteristic orders, as the fault feature vectors, is extracted and normalized from the de-noising signal. Last, in order to diagnose the faults of the gearbox automatically, ELM, provided better generalization performance at a much faster learning speed and with least human intervene, is applied to identify and classify the faults. From the result of experiment, the approach of this paper is effective to judge the fault type under variable speed conditions

Evaluating the road works and street works management permit scheme in Derby, UK

Road works (highway works) and street works (utility works) activities are vital for society to travel, enjoy amenities, and to access essential services such as water, electricity, gas and telecommunications. However, road works and street works can be disruptive, inconvenient and have high social costs. The Permit scheme is a relatively new management regime which seeks to reduce the disruption caused by highway excavations by giving English Street Authorities greater control of works in their areas. The Derby Permit scheme commenced on October 2013. This research aims to understand whether the adoption of the Permit scheme has resulted in any change to the city’s road works and street works landscape. A time series model using an intervention variable was run. 61 months of average works duration data was analysed along with several independent variables including daylight hours, economic activity and precipitation. The results showed that the Permit scheme had a positive effect on Derby by reducing the overall average duration of works by a third of a day. This is a 10% reduction overall, being equal to 8434 days per year, and in monetary terms equivalent to saving £769,048/$1,179,777 in societal costs per annum. This research is significant as it provides impact information for policy makers and practitioners on a relatively new type of scheme, and it is original, in that this is the first time that an intervention analysis approach has been applied to this area of public policy

An Optimal Energy Management Strategy for Hybrid Electric Vehicles

Hybrid Electric Vehicles (HEVs) are used to overcome the short-range and long charging time problems of purely electric vehicles. HEVs have at least two power sources. Therefore, the Energy Management (EM) strategy for dividing the driver requested power between the available power sources plays an important role in achieving good HEV performance. This work, proposes a novel real-time EM strategy for HEVs which is named ECMS-CESO. ECMS-CESO is based on the Equivalent Consumption Minimization Strategy (ECMS) and is designed to Catch Energy Saving Opportunities (CESO) while operating the vehicle. ECMS-CESO is an instantaneous optimal controller, i. e., it does not require prediction of the future demanded power by the driver. Therefore, ECMS-CESO is tractable for real-time operation. Under certain conditions ECMS achieves the maximum fuel economy. The main challenge in employing ECMS is the estimation of the optimal equivalence factor L*. Unfortunately, L* is drive-cycle dependent, i. e., it changes from driver to driver and/or route to route. The lack of knowledge about L* has been a motivation for studying a new class of EM strategies known as Adaptive ECMS (A-ECMS). A-ECMS yields a causal controller that calculates L(t) at each moment t as an estimate of L*. Existing A-ECMS algorithms estimate L*, by heuristic approaches. Here, instead of direct estimation of L*, analytic bounds on L* are determined which are independent of the drive-cycle. Knowledge about the range of L*, can be used to adaptively set L(t) as performed by the ECMS-CESO algorithm. ECMS-CESO also defines soft constraints on the battery state of charge (SOC) and a penalty for exceeding the soft constraints. ECMS-CESO is allowed to exceed a SOC soft constraint when an energy saving opportunity is available. ECMS-CESO is efficient since there is no need for prediction and the intensive calculations for finding the optimal control over the predicted horizon are not required. Simulation results for 3 different HEVs are used to confirm the expected performance of ECMS-CESO. This work also investigates the performance of the model predictive control with respect to the predicated horizon length

Dynamic non-linear system modelling using wavelet-based soft computing techniques

The enormous number of complex systems results in the necessity of high-level and cost-efficient modelling structures for the operators and system designers. Model-based approaches offer a very challenging way to integrate a priori knowledge into the procedure. Soft computing based models in particular, can successfully be applied in cases of highly nonlinear problems. A further reason for dealing with so called soft computational model based techniques is that in real-world cases, many times only partial, uncertain and/or inaccurate data is available. Wavelet-Based soft computing techniques are considered, as one of the latest trends in system identification/modelling. This thesis provides a comprehensive synopsis of the main wavelet-based approaches to model the non-linear dynamical systems in real world problems in conjunction with possible twists and novelties aiming for more accurate and less complex modelling structure. Initially, an on-line structure and parameter design has been considered in an adaptive Neuro- Fuzzy (NF) scheme. The problem of redundant membership functions and consequently fuzzy rules is circumvented by applying an adaptive structure. The growth of a special type of Fungus (Monascus ruber van Tieghem) is examined against several other approaches for further justification of the proposed methodology. By extending the line of research, two Morlet Wavelet Neural Network (WNN) structures have been introduced. Increasing the accuracy and decreasing the computational cost are both the primary targets of proposed novelties. Modifying the synoptic weights by replacing them with Linear Combination Weights (LCW) and also imposing a Hybrid Learning Algorithm (HLA) comprising of Gradient Descent (GD) and Recursive Least Square (RLS), are the tools utilised for the above challenges. These two models differ from the point of view of structure while they share the same HLA scheme. The second approach contains an additional Multiplication layer, plus its hidden layer contains several sub-WNNs for each input dimension. The practical superiority of these extensions is demonstrated by simulation and experimental results on real non-linear dynamic system; Listeria Monocytogenes survival curves in Ultra-High Temperature (UHT) whole milk, and consolidated with comprehensive comparison with other suggested schemes. At the next stage, the extended clustering-based fuzzy version of the proposed WNN schemes, is presented as the ultimate structure in this thesis. The proposed Fuzzy Wavelet Neural network (FWNN) benefitted from Gaussian Mixture Models (GMMs) clustering feature, updated by a modified Expectation-Maximization (EM) algorithm. One of the main aims of this thesis is to illustrate how the GMM-EM scheme could be used not only for detecting useful knowledge from the data by building accurate regression, but also for the identification of complex systems. The structure of FWNN is based on the basis of fuzzy rules including wavelet functions in the consequent parts of rules. In order to improve the function approximation accuracy and general capability of the FWNN system, an efficient hybrid learning approach is used to adjust the parameters of dilation, translation, weights, and membership. Extended Kalman Filter (EKF) is employed for wavelet parameters adjustment together with Weighted Least Square (WLS) which is dedicated for the Linear Combination Weights fine-tuning. The results of a real-world application of Short Time Load Forecasting (STLF) further re-enforced the plausibility of the above technique

Investigating the business process implications of managing road works and street works

Around 2.5 million utility works (street works) occurred in England in 2016 with a construction cost of approximately £2 billion. Comparative figures for highway works (road works) are not readily available, but are expected to be similarly significant. Unsurprisingly, the volume of road works and street works (RWSW) activity in urban areas is considered to have a negative impact on the road network causing disruption and premature deterioration, blighting the street scene, damaging local business trade, and significantly increasing social, economic and environmental costs. Indeed the social costs of street works alone are estimated to be around £5.1 billion annually. Despite the economic significance of highway infrastructure, the subject of road works and street works management is under-researched, with greater research emphasis on technology-based, as opposed to policy-based management approaches. Consequently, the aim of this study was to investigate the efficiency and effectiveness of managing the business process of RWSW. Due to limited academic literature in the subject domain, earlier research focused on identifying the industry actors, their motivations, as well as drivers and barriers to RWSW management. Semi-structured interviews with industry stakeholders highlighted the industry s complexity and revealed that several issues contributed to ineffective RWSW management. Principal problems included Street Authorities (SA) failing to take enough ownership of the RWSW coordination process, highway legislation not encouraging joint working due to inherent challenges arising from reinstatement guarantees, and entrenched attitudes and adversarial practices in the construction industry encouraging silo working. The Derby Permit Scheme (legislative tool) was intended to improve RWSW management through giving SAs greater control of highway works. Accordingly, RWSW activity was tested through a statistical time series intervention analysis to separately examine the impacts of the Highway Authority (HA) led works and utility industry led works over 6.5 years. The Permit Scheme was found to reduce utility works durations by around 5.4%; equivalent to 727 days, saving between £2.1 - £7.4 million in construction and societal costs annually. Conversely, the Permit Scheme did not noticeable reduce the HA led works. Instead, the introduction of a works order management system (WOMS) to automate some of the back office road works process was found to reduce works durations by 34%; equivalent to 6519 days and saving between £8.3 - £48.3m per annum. This case study highlighted that more considered practices were required by the HA to reduce RWSW. The stakeholder study and the automated WOMS technology found that well-managed business processes tended to lead to better executed highway works on-site. Informed by these experiences, the sponsor was keen to re-engineer its internal business processes. Business process mapping was adopted to identify inefficient practices and improved coordinated working opportunities on three key internal teams involved in the road works process. Findings revealed that silo working was inherent and that processes were built around fragmented and outdated Information Technology (IT) systems, creating inefficiencies. A subsequent validation exercise found that certain practices, such as restricted data access and hierarchal management styles were culturally embedded and also common across other local authorities. Peer reviewed recommendations to improve working practices were made, such as adopting an integrated Highways Management IT system, vertical integration between the customer relationship management IT system and the Highways IT systems, and the provision of regulatory training. In conclusion, based on the finding of this study, a generic logic map was created with potential to transfer the learning to other local authorities and for their use when evaluating road works administrative processes

Novel methods in retinal vessel calibre feature extraction for systemic disease assessment

Retina and its vascular network have unique branching characteristics morphology of which will change as a result of some systemic diseases, including hypertension, stroke and diabetes. Therefore, retinal image has been used as non-invasive screening window for risk assessment and prediction of such disease condition especially at the baseline. The assessment is based on a number of features among which vessel diameter (both individual and summary) and fractal dimension (FD) are the ones mostly associated with risk of diabetes and stroke. The association is linked to the higher risk of diabetes and stroke in people with narrower retinal arteriole diameter or change in overall fractal dimension independent of any risk factor (i.e. blood pressure, cardiovascular risk factors). Diameter measurement requires vessel edges to be located and tracked however; accurate edge perception is subject to image contrast, shadows, lighting condition and even presence of retinopathy legions close to vessel boundaries. This will lead to imprecision and inconsistencies between different automatic measurement techniques and may affect the significance of its association with disease condition in risk-assessment studies. As accuracy and success of diameter measurement is subject to large variations due to image artifacts it may not be suitable for fully automatic applications. In order to compensate for such error, at first two novel automatic vessel diameter measurement techniques were proposed and validated which were more robust in the presence of such image artifacts compared to similar methods. However, sometimes the exact edge location and actual diameter value is not of interest. In most case-control studies, it is of importance to comparatively evaluate the variations in retinal vessel diameter as a sign of retinopathy such as arteriolar nicking as an example of hypertensive retinopathy. Vessel diameter is often required to be compared with a reference value in many analytical assessments for diagnostic purpose. This includes monitoring the diameter variations of a specific vessel segment within single subject overtime or across multiple subjects. This helps ophthalmologists to understand whether it has undergone any significant change and perhaps associate it with a disease abnormality. A technique that can effectively quantify that change without being impaired by image artifacts is of more importance and one of the rationales of this study. This research hypothesized an edge independent solution for quantifying diameter variations when the actual diameter value is not required and proposed a new feature based on fractal analysis of vessel cross-section profile as a time series signal. This feature provides a link between FD as a global measure of the complexity and diameter variation as local property of a specific vessel segment. The clinical application of this feature has been validated on two population studies which showed promising result for assessment of mild non-proliferative diabetic retinopathy and 10-year stroke. This research work has also investigated whether the FD of retinal microvasculature would be affected by cyclic pulsations of retinal vessels and whether ECG synchronization is required prior to taking fundus images to compensate for this potential source of variations

Phénologie et dynamique de population des crustacés décapodes : effets de facteurs biotiques et climatiques sur le recrutement du crabe des neiges (Chionoecetes opilio)

RÉSUMÉ: Chez les invertébrés marins benthiques, la variabilité du recrutement des premiers stades de vie (c.-à-d., œuf, larve et juvénile) peut jouer un rôle prépondérant dans la dynamique des populations. En effet, les premiers stades de vie sont généralement les plus sensibles aux variations de l'environnement, de sorte que de légers changements des conditions environnementales peuvent leur imposer des taux de mortalité très variables. Toutefois, l'étude du recrutement des premiers stades de vie s'avère être très complexe en milieu naturel, puisque ce processus est sous le contrôle d'une multitude de facteurs abiotiques et biotiques qui interagissent à différentes échelles spatio-temporelles. De plus, les premiers stades de vie de nombreuses espèces marines ne sont pas échantillonnés ou, le cas échéant, sont échantillonnés souvent de manière inefficace en raison de leur petite taille et de leur répartition méconnue. Ainsi, le rôle relatif des facteurs biotiques et abiotiques dans la dynamique des populations d'invertébrés benthiques demeure peu connu chez la plupart des espèces. Ce projet avait pour objectif principal d'examiner l'effet de plusieurs facteurs climatiques et biotiques sur la phénologie et la dynamique de population d'un invertébré marin exploité commercialement dans le golfe du Saint-Laurent et ailleurs dans le nord de l'Atlantique et du Pacifique, soit le crabe des neiges (Chionoecetes opilio). Un vif débat persiste dans le cas du crabe des neiges quant à savoir si les variations importantes de son abondance sont dues principalement à des mécanismes de contrôle ascendants ou descendants. D'une part, la variabilité importante de la biomasse commerciale du crabe des neiges pourrait découler d'une production ou d'une survie très variable des premiers stades de vie engendrée par les variations du climat. Une autre école de pensées, au contraire, suppose un contrôle descendant par prédation des poissons de fond. De plus, la ou les causes des cycles d'abondance observés chez certaines populations du crabe des neiges sont toujours méconnues. Ce phénomène pourrait être dû à des facteurs de régulation intrinsèques, comme le cannibalisme. Les résultats obtenus dans cette thèse suggèrent fortement que l'importante variabilité du recrutement des premiers stades de vie du crabe des neiges serait due principalement à des processus ascendants associés à la variabilité du climat. Nous avons trouvé que les conditions de glace durant l'hiver précédant la phase larvaire ont influencé l'abondance larvaire du crabe des neiges via ses effets probables sur les conditions du bloom planctonique. Nous avons aussi trouvé que la température de l'eau près de la surface durant la phase larvaire et sur le fond durant la phase benthique étaient des facteurs déterminants qui influencent l'abondance des larves et des premiers stades benthiques du crabe des neiges. Ce résultat confirme nos hypothèses et supporte l'idée que les premiers stades de vie, particulièrement les premiers stades benthiques, sont très sensibles à de faibles variations de la température de l'eau. Nous n'avons trouvé aucune évidence d'un contrôle descendant via la prédation par les poissons de fond sur les premiers stades benthiques du crabe des neiges qui soit de nature à dicter des tendances démographiques perceptibles à court ou à long terme. Nos résultats ont également mis en évidence le rôle important des facteurs dépendants de la densité dans la dynamique de population du crabe des neiges. Cette étude est la première à démontrer une relation entre la biomasse reproductrice (production larvaire) et le recrutement chez le crabe des neiges. Le cannibalisme par les crabes de taille intermédiaire était aussi un des principaux facteurs biotiques déterminant la variabilité interannuelle de l'abondance des premiers stades benthiques du crabe des neiges. D'ailleurs, la modélisation de la dynamique de population a fortement suggéré que le cannibalisme est responsable des cycles d'abondance d'environ 8 ans observés dans les populations du crabe des neiges étudiées dans le cadre de cette thèse. Notre étude a également révélé un changement important dans la phénologie larvaire du crabe des neiges en réponse aux changements climatiques. Depuis le début des années 1990, le golfe du Saint-Laurent s'est réchauffé, ce qui a entraîné une phénologie plus hâtive au cours des 30 dernières années. Ce changement dans la phénologie larvaire du crabe des neiges est probablement une conséquence de l'effet du réchauffement climatique sur la période d'éclosion des larves ainsi que sur le taux de développement larvaire. Le réchauffement des températures de l'eau a aussi été responsable, du moins en partie, de la diminution de l'abondance des larves et des premiers stades benthiques du crabe des neiges au cours de la période d'étude. Ces résultats suggèrent fortement que l'abondance des espèces sténothermes d'eau froide telles que le crabe des neiges pourrait diminuer davantage si le réchauffement dans le golfe du Saint-Laurent se poursuit. Cette thèse apporte une contribution significative à notre compréhension de la dynamique de population d'un invertébré marin écologiquement et économiquement important des écosystèmes benthiques de l'hémisphère nord. Nos recherches ont montré que les processus ascendants et densité-dépendants prévalent sur les processus de contrôle descendants pour établir les fluctuations à court terme (cycles) et les tendances à plus long terme du recrutement des premiers stades de vie du crabe des neiges. Les résultats obtenus et les méthodologies développées dans cette thèse pourront entraîner un raffinement des protocoles de gestion des populations exploitées du crabe des neiges et ainsi contribuer à une meilleure conservation de l'espèce, particulièrement dans le contexte actuel de changement climatique. -- Mot(s) clé(s) en français : crabe des neiges; dynamique de population; recrutement; premiers stades de vie; phénologie larvaire; processus de contrôle ascendants; variabilité climatique; cannibalisme; production larvaire. -- ABSTRACT: In marine benthic invertebrates, variability in the recruitment of early life stages (i.e. embryos, larvae and juveniles) may play a major role in population dynamics. In fact, early life stages are generally more sensitive to environmental changes than later life stages, so that small changes in environmental conditions can cause highly variable mortality rates. However, the study of recruitment of early life stages is proving to be very complex, since this process is under the control of numerous abiotic and biotic factors that can interact at different spatio-temporal scales. In addition, the early life stages of many marine species are either not sampled or, the case arising, are sampled often inefficiently due to their small size and unknown distribution. Thus, the relative role of biotic and abiotic factors in the dynamics of benthic invertebrate populations remains poorly understood in most species. The main objective of this project was to examine the effect of several climatic and biotic factors on the phenology and population dynamics of snow crab (Chionoecetes opilio), a marine benthic invertebrate that is commercially exploited in the Gulf of St. Lawrence and elsewhere in the North Atlantic and Pacific. A lively debate persists in the case of snow crab as to whether the strong variations in its abundance are due mainly to bottom-up or top-down effects. On the one hand, the strong fluctuations in commercial snow crab biomass could be the result of a highly variable production or survival of early life stages caused by bottom-up processes associated with climate variability. Another school of thought, on the other hand, supposes a top-down control by predation of groundfish. In addition, the causes of the abundance cycles observed in some snow crab populations are still unknown, but could be due to intrinsic regulatory factors such as cannibalism. The results obtained in this thesis strongly suggest that the high variability in the recruitment of snow crab early life stages is mainly due to bottom-up processes associated with climate variability. We found that ice conditions during the winter prior to the larval phase influenced larval abundance of snow crab through its likely effects on plankton bloom conditions. We also found that near-surface water temperature during the larval phase and bottom water temperature during the benthic phase were important factors influencing the abundance of larvae and early benthic stages of snow crab. These results confirm our hypotheses and support the idea that early life stages, particularly early benthic stages, are very sensitive to small variations in water temperature. We found no evidence of a top-down control through groundfish predation on the early benthic stages of snow crab. Our results also highlighted the important role of density-dependent factors in the population dynamics of snow crab. This study is the first to demonstrate a significant relationship between reproductive biomass (larval production) and recruitment in snow crab. Intercohort cannibalism was also one of the main biotic factors determining the interannual variability in abundance of the early benthic stages of snow crab. Moreover, modeling of snow crab population dynamics strongly suggested that cannibalism is responsible for the 8-year abundance cycles observed in the snow crab populations studied in this thesis. Our study also revealed a significant change in the larval phenology of snow crab in response to climate change. Since the early 1990s, the Gulf of St. Lawrence has warmed, which resulted in earlier phenology over the last 30 years. This change in snow crab larval phenology is likely a consequence of the effect of global warming on the larval hatching period as well as the larval development rate. Warming water temperatures were also responsible, at least in part, for the decrease in larval and early benthic abundance of snow crab during the study period. These results strongly suggest that the abundance of cold-water stenothermic species such as snow crab could decrease further if warming in the Gulf of St. Lawrence continues. This thesis makes a significant contribution to our understanding of the population dynamics of an ecologically and economically important marine invertebrate of benthic ecosystems in the northern hemisphere. Our research has shown that bottom-up and density-dependent processes dominate top-down processes to establish short-term fluctuations (cycles) and longer-term trends in recruitment of early life stages of snow crab. The results obtained and the methodologies developed in this thesis may lead to a refinement of the management protocols of exploited populations of snow crab and thus contribute to a better conservation of the species, particularly in the current context of climate change. -- Mot(s) clé(s) en anglais : snow crab; population dynamics; recruitment; early life stages; larval phenology; bottom-up processes; climate variability; cannibalism; larval production

Applied time series analysis and innovative computing

This text is a systematic, state-of-the-art introduction to the use of innovative computing paradigms as an investigative tool for applications in time series analysis. It includes frontier case studies based on recent research