Automatedguidedvehicles (AGVs), a type of unmanned moving robots that move along fixed routes or are directed by laser navigation systems, are increasingly used in modern society to improve efficiency and lower the cost of production. A fleet of AGVs operate together to form a fully automatic transport system, which is known as an AGV system. To date, their added value in efficiency improvement and cost reduction has been sufficiently explored via conducting in-depth research on route optimisation, system layout configuration, and traffic control. However, their safe application has not received sufficient attention although the failure of AGVs may significantly impact the operation and efficiency of the entire system. This issue becomes more markable today particularly in the light of the fact that the size of AGV systems is becoming much larger and their operating environment is becoming more complex than ever before. This motivates the research into AGV reliability, availability and maintenance issues in this thesis, which aims to answer the following four fundamental questions: (1) How could AGVs fail? (2) How is the reliability of individual AGVs in the system assessed? (3) How does a failed AGV affect the operation of the other AGVs and the performance of the whole system? (4) How can an optimal maintenance strategy for AGV systems be achieved?
The preventive maintenance program is the key to any attempt to improve the maintenanceperformance. It reduces the amount of reactive maintenance to a level that allows other practices to be effective. This is achieved by having plans/schedules for equipment to carry out standard maintenance activities such as lubrication tasks, replacement of worn out parts, checking of blockages, oil levels, etc. The preventive maintenance schedules are designed to strike a measured balance between achieving high equipment reliabilitythrough considerable lowering of failures and optimizing equipment down time that inevitably results from planned preventive maintenance. Systematic or scheduled maintenance, such as lubrication or replacement is done at regular intervals.
mation is to be processed in order to arrive at a better choice. AGV selection attribute is defined as a factor that influences the selection of an AGV for a given application. These attributes include: costs involved, floor space requirements, load carry- ing capacity, travel speed, lift height, turning radius, travel pat- terns, programming flexibility, labor requirements, expandabil- ity, ease of operation, maintenance aspects, payback period, re- configuration time, hardware targebility, support for modular- ity (i.e. ease of integrating with FMS, ASRS, and robots), ro- bustness etc. AGV manufacturers develop AGVs of a variety of types and sizes. One company does not cover all niches equally well. Therefore, it is wise to evaluate AGVs based on a num- ber of attributes. The decision making process in the selec- tion of AGV consists of a combination of a variety of multi- dimensional attributes (see, Fig. 1). It includes: performance parameters, technical specifications, economical considerations and future expansion issues. One company does not cover all attributes equally well. Therefore, it is wise to evaluate AGVs based on a number of attributes.
This net describes the process undertaken once an AGV fails and needs to be removed to the maintenance site. For space reasons the net has not been included here but is described briefly below, more detail can be found in . Once an AGV fails and a token resides in the ‘mission failed’ place in the MPN the recycling of the AGV starts immediately. Initially the position of the failed vehicle is located and the route for the recycle vehicle to take from the maintenance site to the failed vehicle is optimized. If any AGV is found running on the route the recycle vehicle will not leave the maintenance site until that AGV reaches its next station where it will then park and be removed from the route. Any other vehicles will stop in order to avoid possible blockage. After the recycle vehicle reaches the position of the failed AGV it will tow it back to the maintenance site. As the site can be reached by following the flow of working AGV’s all other vehicles restart their mission once the recycle vehicle has collected the failed AGV. As the recycling of failed AGV’s disturbs the normal operation of other AGV’s the optimal routing of the recycling vehicle is crucial to the performance of the multi-AGV system. In this work the optimal path has been obtained using a forward-tracking search algorithm which is described in .
of their failure?’, have not been answered. To answer these questions, in 2013 Duran and Zalewski tried to identify the basic failure modes of the light detection and ranging (LIDAR) system and the camera-based computer vision system (CV) on AGVs by the combined use of Fault Tree Analysis (FTA) and Bayesian Belief Network approach (BN). In that work, human injury, property damage and vehicle damage were defined as the top events in the fault tree. However, the research did not cover all components and subassemblies in AGVs. Considering a complete investigation of the safety and reliability issues of all AGV components and subassemblies is fairly important not only to ensure the high reliability and availability of AGVs and their success of delivering prescribed tasks but also to optimize their maintenance strategies. Complementary research is conducted in this paper, in which a promising technical approach will be established to identify the critical risks of all AGV components and the crucial mission phases in AGV operation. In this paper, Failure Modes and Effects Criticality Analysis (FMECA) and Fault Tree Analysis (FTA) will be used in combination for achieving such a purpose.
degree of AGVs can be maximised at the same time ; Wu and Zhou created a simulation model to avoid collisions, deadlock, blocking and minimise the route distance as well with a coloured resource-oriented Petri Net . However, little effort has been made to investigate the safety and reliability issues of the AGV components/subassemblies and their probability of success in completing a prescribed mission. Although Fazlollahtabar recently created a model to maximise the total reliability of the AGVs and minimise the repair cost of AGV systems , they considered the AGV as a whole. Hence, fundamental questions, such as ‘How could AGVs fail?’ and ‘What are the possibilities of their failure?’, have not been answered.To answer these questions, Duran and Zalewski tried to identify the basic failure modes of the light detection and ranging (LIDAR) system and the camera-based computer vision system (CV) on AGVs in 2013 by the approach of Fault Tree Analysis (FTA) and Bayesian Belief Networks (BBN) . In that work, human injury, property damage and vehicle damage were defined as the top events in the fault tree. However, the research did not cover all components and subassemblies in AGVs. A complete investigation of the safety and reliability issues of all AGV components and subassemblies is important not only to ensure the high reliability and availability of AGVs and their success of delivering prescribed tasks, but also to optimise their maintenance strategies. Research is conducted in this paper to identify the critical risks of all AGV components and the crucial mission phases in an AGV operation. Failure Modes Effects and Criticality Analysis (FMECA) and Fault Tree Analysis (FTA) will be adopted to achieve this. Hence, the contribution of this paper is in developing an efficient approach to investigate the reliability of AGVs taking into account the profiles of the mission undertaken.
The complexity within the railway system lies not in any technical aspect, although these are complicated, but in the interactions and relationships between the different stakeholders; aligning their often competing goals in order to deliver the system goal - safely transporting passengers and freight on time. This becomes especially important with the whole-life approach and requires greater co- operation and communication between all stakeholders.
The article substantiates that the main issues associated with determining the reliability rating of banking and non-bank financial institution: not regulating the issue of creating a unified system of supervision of finan- cial intermediaries; closed ratings of financial intermediaries, which are expected by state regulators; lack of transparent information on the activities of financial intermediaries; the need to create a single rating system of transparency on the main indicators of the activities of financial intermediaries.
As mentioned in section 1.2, the simultaneous scheduling of AGVs and machines in FMS environments has been widely regarded in the literatures. In this kind of problem a sequence of desired tasks are considered to be completed on various machines of FMS. AGVs are scheduled in a way that total traveling time of AGVs and operational time of machines is minimized. Many of the literatures proposed mathematical formulations to solve this problem (Bilge and Ulusoy, 1995; Jawahar et al., 1998, El Khayat, 2006). Incidentally most of the literatures showed that genetic algorithms (GAs) can be considered as an optimization tool to improve the performance of the results of such kind of problem (Ulusoy et al., 1997, Abdelmaguid et al., 2004; Reddy and Rao, 2006).
While these two reasons suggest that implementing AVs into transportation systems should be carefully executed, they do not get at a fundamental assumption of modern technology that Hans Jonas (1984) previously identified. Namely, that we must rid our- selves of the idea that technology without limitation, viewing it as the vehicle that could guide us to utopia, is inherently good, holding that we should balance such an outlook with a fear of an ecologically unsound dystopia that would imperil humankind (Jonas 1984). 5 Yet, the fact remains: we must rely on new and emerging technologies to deliver challenges that driverless vehicles will present. The underscored point here is that AV’s benefits will make it worthwhile to develop solutions and/or workarounds to such challenges. For instance, one can argue that we would not need ethical frameworks or technical debates about how to incorporate AVs into society if the benefits were not assumed to be worth the cost of restructuring urban mobility. For a good example of the technical challenges, see Watzenig and Horn (2017). Automated driving: safer and more
controller to navigate the robot. In this way, the robot navigates along the path while avoiding obstacles unknown to the model. This approach is thus able to achieve the optimality of model-based approaches and the reactivity of sensor-based approaches. This paper brings together much of the key research work on the overall controls of sensors and planning which was inspired during the AGV development. The paper focuses on the development of fuzzy control logic that improves the AGV’s performance.
For real life experiments, the discussed software frame- work architecture is deployed on three different types of om- nidirectional AGVs without the need for any adoption of the software. One AGV was build by the IMSL team (see Fig. 3), while the second AGV is a commercial system targeted at educational and scientific facilities. A third type of AGVs are commercial systems used in industrial environments. While only the self build omnidirectional AGV is equipped with an electronic load lift suitable for AGV scenarios, all AGVs feature a Mecanum wheel based omnidirectional drive. With this holonomic drive, all AGVs can make use of all three, the two coordinate degrees and the orientation of the AGV, degrees of freedom on the plane (see Fig. 4). Not only the AGVs can drive sideways, they also can freely change their orientation while driving in one direction on a straight line. Combining the localization engine and the electronic load lift, the self build AGV can autonomously load and unload euro bins through specially crafted stations. In this scenario the AGV uses the omnidirectional drive and the precise positioning information provided by the localization engine to access a station. Using the electronic load lift either a euro bin formally transported by the AGV is then unloaded to the station or a euro bin resting on top of the station is picked up by the AGV.
Manual distraction is concerned with the arm posture of drivers. Manual distraction has limited research in the manual driving field; though, (Craye and Karray, 2015) proved that arm posture is the highest cue for driver distraction in their study. A Kinect camera was used to extract four arm postures and used Hidden Markov Model and AdaBoost classifiers to fusion PERCLOS, head pose, orientation, and expressions together. The Hidden Markov Model is a statistical model that observes previous states to predict how likely the next state would be. AdaBoost is a machine learning meta- algorithm that merges several other machine algorithms to improve their overall performance. The Kinect camera made it easier to classify since it adds a depth layer into the RGB images making it easier to extract features from data. The highest accuracy in the study was 89% at estimating driver distraction. Another study by Park and Trivedi, (2005) used body poses including driver’s static pose, dynamic gesture, body-part action, and driver-vehicle interaction to build an activity recognition framework for driver’s activities. While arm positioning was essential for the classification in this study, the trend of research in this field focuses more on facial features and gaze estimation.
Extending these advantages of industrial trucks by means of automation technology results in increased reliability and reduced operating costs. The outcome is the so called AutomatedGuided Vehicle System, abbreviated as AGVS. AGVS are capable of performing transportation tasks fully automated at low expenses. Applications can be found throughout all industrial branches, from the automotive, printing and pharmaceutical sectors over metal and food processing to aerospace and port facilities. The increasing interest in AGVS is reflected in the sales figures which reached a new peak in 2006.
7.5 Conclusions about condition monitoring for gearboxes In addition to what was mentioned in the previous conclusion about gearboxes, gears and bearings seldom break down spontaneously. Instead they are subjected to a wear process. Today there are many methods of monitoring the gearbox and the closely connected bearings. The problem that still appears to be unsolved is the exact wear process for gearboxes within wind turbines. The manufacturers of condition monitoring systems will not explicitly claim that their products can predict the lifetime of the components but they are using terms as “risk of failure” . Therefore it is impossible to set any definitions of the different stages of the wear and consequently it is difficult to set up schedules for maintenance based on the actual condition of the gearbox. The available equipment on the market today can tell when a preset limit for the condition is reached and can warn the user of a possible failure, but what the system cannot do is to fully predict the remaining lifetime of the component. To be able to predict a lifetime close to the real one a lot of measures and comparisons with similar systems in similar environments is required.
Abazari et al.  mentioned the number of possible so- lutions some of which may not be feasible with respect to the constraints of system such as capacity of ma- chines/cells and capacity of tool magazine. Computing all these solutions to determine the optimum one is computationally intractable for medium- to large-sized problems. In addition, it is the fact that MLP of a FMS is recognized for its complexity . Moreover, the MLP related to automated manufacturing system belongs to the classication of NP-hard problems where the computational solution times are non-polynomial in the size of the problem [67-72]. Due to this fact, we need to employ a meta-heuristic search algorithm to solve it. Therefore, two meta-heuristic algorithms are employed as well to enable the validation of the results obtained. In the next two subsections, brief descriptions are rst given for GA and ACO.
New strategies and methods for assessing the security of protection systems to reduce the risk of unnecessary disturbances and blackouts are the main topic of the present paper. The system be- havior of a protection system and network is analyzed and assessed as a whole. Hence, the estab- lished algorithms are capable to handle complex network structures with regard to an intelligent data management as well as data validation. Protection security assessment comprised two dif- ferent aspects: on the one hand the behavior regarding dependability and security in terms of speed and sensitivity, on the other hand the behavior regarding the response on dynamic network phenomena as voltage stability and transient stability. A new automated method for assessing the dependability and security of protection systems is shown. The short-circuit simulation tool is used to provide a simulation system including network and protection devices as a whole. The handling of the large amount of resulting data is done by an intelligent visualization method like a “fingerprint” analysis. Further on the paper is focused on the protection response on dynamic network phenomena and presents innovative strategies for this investigation aspect. The struc- ture of simulation environment will be described. Results of a case study show the application of this method on a real network. The system tool which is concluding these two aspects of protec- tion assessment is called SIGUARD ® PSA.
The 2800 and 2810 use a "pusher" fan which pull air in through the sides of the engine hood and pushes it through the coolers and the lou- vered grill (Fig. 57). This system allows the air to be "filtered" through the hood's screening be- fore it reaches the coolers. This also keeps hot air and dust off the operator. Depending on the conditions the coolers will most likely need to be cleaned at some point. Since any debris will collect on the engine side of the coolers the de- bris must be cleaned by forcing the debris off from the other side of the coolers. Air or water under pressure works best for thorough clean- ing.