Efficient implementation of hierarchical resource control for multi-agent systems

Development of the World Wide Web makes it possible for multiple computers to work together in order to solve problems and make the most efficient use of resources. A distributed system is composed of such computers which are separately located and connected with each other through a network. One paradigm for computation using distributed systems is the multi-agent systems, in which many autonomous agents interact with each other to solve problems. The agents in a multi-agent system may be distributed on different computers (or nodes), where each computer owns its resources. Although the resources in a multi-agent system are connected by a network through which mobile agents can migrate for accessing sufficient resources, how to share these independently owned resources in both an effective and an efficient way is not fully understood. A key challenge in multi-agent systems is how to account for and control the resources which are located on individual nodes. The CyberOrgs model offers one approach to manage resources among competitive or collaborative agents by organizing computations and resources in a hierarchy. A cyberorg encapsulates agents and resources in a boundary and distributes the resources available to it within this boundary. A cyberorg contained in another cyberorg has a contract with the outer cyberorg, according to which it receives resources that it may use. A cyberorg also encapsulates an amount of the eCash, which is the currency for purchasing resources from its host cyberorg. Therefore, cyberorgs have a hierarchical structure in which resources are delivered to computations by a process where resources flow down from the root to the leaves of the hierarchy and the eCash flows up from the leaves toward the root. However, the hierarchical structure of the CyberOrgs model presents challenges in scalability. As a result, efficiency is an important concern in the implementation of CyberOrgs. In this thesis, an efficient implementation of the CyberOrgs model is described. System design, APIs of the implementation, example applications, experimental results, and future directions are presented

Coordinating Resource Use in Open Distributed Systems

In an open distributed system, computational resources are peer-owned, and distributed over time and space. The system is open to interactions with its environment, and the resources can dynamically join or leave the system, or can be discovered at runtime. This dynamicity leads to opportunities to carry out computations without statically owned resources, harnessing the collective compute power of the resources connected by the Internet. However, realizing this potential requires efficient and scalable resource discovery, coordination, and control, which present challenges in a dynamic, open environment. In this thesis, I present an approach to address these challenges by separating the functionality concerns of concurrent computations from those of coordinating their resource use, with the purpose of reducing programming complexity, and aiding development of correct, efficient, and resource-aware concurrent programs. As a first step towards effectively coordinating distributed resources, I developed DREAM, a Distributed Resource Estimation and Allocation Model, which enables computations to reason about future availability of resources. I then developed a fine-grained resource coordination scheme for distributed computations. The coordination scheme integrates DREAM-based resource reasoning into a distributed scheduler, for deciding and enforcing fine-grained resource-use schedules for distributed computations. To control the overhead caused by the coordination, a tuner is implemented which explicitly balances the overhead of the control mechanisms against the extent of control exercised. The effectiveness and performance of the resource coordination approach have been evaluated using a number of case studies. Experimental results show that the approach can effectively schedule computations for supporting various types of coordination objectives, such as ensuring Quality-of-Service, power-efficient execution, and dynamic load balancing. The overhead caused by the coordination mechanism is relatively modest, and adjustable through the tuner. In addition, the coordination mechanism does not add extra programming complexity to computations

Planetary gearbox fault diagnosis using morphological gradient filters

As a key component of rotating machineries, fault diagnosis for planetary gearbox is very difficult compared to the fixed shaft gearbox. It is becoming a hot research topic recent years. Different fault type has different vibration characteristics. Different from the traditional signal analysis methods, morphological gradient filters are used to extract the fault frequencies in this paper. Planetary gearbox experiment signals are used to validate the proposed method

A recognition method of plunger wear degree of plunger pump using probability neural network

In order to increase the diagnosis efficiency of plunger wear fault, a recognition method is developed using sensitivity analysis and probability neural network. Firstly, 17 time domain characteristics of vibration signal are extracted. Then analyzed the sensitivity of characteristics to failure to select sensitive characteristics parameters. Finally, PNN method to identify the degree of plunger wear was proposed. A hydraulic pump fault simulation experiment was designed, and validated the proposed method by experimental data. The results show that the method can quickly and effectively identify the degree of plunger wear

Inspection period determination for two-stage degraded system

At present studies on degradation process are mainly single stage degradation mode, however, in practice the system degradation process is generally multi-stage. Based on general degradation process modeling, the paper assumed degenerate distribution of two-stage mode obey various normal distribution, shock times obey Poisson process. Reliability modeling and mean time to failure modeling of two-stage degraded mode are studied. Functional check period determination methods are used to calculate inspection periods for different degradation stage. In numerical example, inspection periods for system with two-stage degradation process are analyzed

Spare support model based on gamma degradation process

Spare parts ordering is very important in the domain of system support based on condition-based maintenance. For a single-unit system with condition monitoring, a joint degradation and spare parts ordering model is established in this paper to achieve the lowest total cost rate as the objective. The degradation process of system is assumed to be followed a gamma process. A decision on optimal spare ordering time by the improved cost rate model based on the proposed degradation model is made. Finally, a case analysis is implemented to demonstrate the effectiveness of the proposed model in this paper. Analysis results show that the proposed model can reduce the cost rate effectively

Gear fault diagnosis and damage level identification based on Hilbert transform and Euclidean distance technique

This paper deals with the problem of gear fault diagnosis with multiple possible fault modes and damage levels. Gears are the most essential parts in rotating machinery. Their health status is a significant index to indicate whether machines can run continually or not. So, gear fault diagnosis and damage level identification is very important in engineering practice. An accuracy way to identify the state of gears is urgently needed for the maintenance decision making. In this paper, a novel gear fault diagnosis and damage level identification method based on Hilbert transform (HT) and Euclidean distance technique (EDT) is developed. The energies of six frequency bands are used as the fault feature through the contrast with other two parameters, kurtosis and skewness. Then HT is used to obtain analytic signal. Finally, EDT is utilized to recognize the different fault modes and damage levels. This method is implemented by two stages, i.e., classifying different fault modes and identifying damage levels for every fault mode. The effectiveness of this methodology is demonstrated by compare to fisher discriminant analysis (FDA) using experiment data acquired from a real gearbox. In addition, industrial data is also used to validate the effectiveness of the proposed method

Fast Sequence Component Analysis for Attack Detection in Synchrophasor Networks

Modern power systems have begun integrating synchrophasor technologies into part of daily operations. Given the amount of solutions offered and the maturity rate of application development it is not a matter of "if" but a matter of "when" in regards to these technologies becoming ubiquitous in control centers around the world. While the benefits are numerous, the functionality of operator-level applications can easily be nullified by injection of deceptive data signals disguised as genuine measurements. Such deceptive action is a common precursor to nefarious, often malicious activity. A correlation coefficient characterization and machine learning methodology are proposed to detect and identify injection of spoofed data signals. The proposed method utilizes statistical relationships intrinsic to power system parameters, which are quantified and presented. Several spoofing schemes have been developed to qualitatively and quantitatively demonstrate detection capabilities.Comment: 8 pages, 4 figures, submitted to IEEE Transaction

Bearing fault diagnosis and degradation analysis based on improved empirical mode decomposition and maximum correlated kurtosis deconvolution

Detecting periodic impulse signal (PIS) is the core of bearing fault diagnosis. Earlier fault detected, earlier maintenance actions can be implemented. On the other hand, remaining useful life (RUL) prediction provides important information when the maintenance should be conducted. However, good degradation features are the prerequisite for effective RUL prediction. Therefore, this paper mainly concerns earlier fault detection and degradation feature extraction for bearing. Maximum correlated kurtosis deconvolution (MCKD) can enhance PIS produced by bearing fault. Whereas, it only achieve good effect when bearing has severe fault. On the contrary, PIS produced by bearing weak fault is always masked by heavy noise and cannot be enhanced by MCKD. In order to resolve this problem, a revised empirical mode decomposition (EMD) algorithm is used to denoise bearing fault signal before MCKD processing. In revised EMD algorithm, a new recovering algorithm is used to resolve mode mixing problem existed in traditional EMD and it is superior to ensemble EMD. For degradation analysis, correlated kurtosis (CK) value is used as degradation indicator to reflect health condition of bearing. Except of theory analysis, simulated bearing fault data, injected bearing fault data, real bearing fault data and bearing degradation data are used to verify the proposed method. Simulated bearing fault data is used to explain the existed problems. Then, injected bearing fault data and real bearing fault data are used to demonstrate the effectiveness of proposed method for fault diagnosis. Finally, bearing degradation data is used to verify the degradation feature CK extracted based on proposed method. All these case studies show the effectiveness of proposed fault diagnosis and degradation tracking method

Bearing prognostics with non-trendable behavior based on shock pulse method and frequency analysis

Bearings are one of the most important parts of rotating machineries. Their fault diagnosis and prognosis are critical for the maintenance decision making. In reality, few bearings are working under constant operating conditions. So, robust features which are not sensitive to the operating condition are needed for bearing prognostics. Sometimes, even if they are working under stationary conditions, common-used degradation features are non-trendable and cannot be used to predict the remaining useful lives. In order to address these two issues, shock pulse method and frequency analysis are combined to detect the incipient fault and predict the remaining useful lives. Maximum normalized shock value which is extracted using shock pulse method can reflect the degradation process more robust under non-stationary conditions. And frequency analysis can identify the change points of degradation states when degradation features are non-trendable. Finally, a case study is conducted where the proposed methods are demonstrated by analyzing the 2012 PHM challenge data sets