A hybrid neural classifier for dimensionality reduction and data visualization and its application to fault detection and classification of induction motors

In this paper, a hybrid neural classifier combining the auto-encoder neural network and the Lattice Vector Quantization (LVQ) model is described. The auto-encoder network is used for dimensionality reduction by projecting high dimensional data into the 2D space. The LVQ model is used for data visualization by forming and adapting the granularity of a data map. The mapped data are employed to predict the target classes of new data samples. To improve classification accuracy, a majority voting scheme is adopted by the hybrid classifier. To demonstrate the applicability of the hybrid classifier, a series of experiments using simulated and real fault data from induction motors is conducted. The results show that the hybrid classifier is able to outperform the Multi-Layer Perceptron neural network, and to produce very good classification accuracy rates for various fault conditions of induction motors.

Multi-level virtual ring : an architecture for content routing in wireless sensor network

Two main problems prevent the deployment of content delivery in a wireless sensor network: the address, which is widely used in the Internet as the identifier, is meaningless in wireless network, and the routing efficiency is a big concern in wireless sensor network. This paper presents an embedded multi-level ring (MVR) structure to address those two problems. The MVR uses names rather than addresses to identify sensor nodes. The MVR routes packets on the name identifiers without being aware the location. Some sensor nodes are selected as the backbone nodes and are placed on the different levels of the virtual rings. MVR hashes nodes and contents identifiers, and stores them at the backbone nodes. MVR takes the cross-level routing to improve the routing efficiency. Further, MVR is constructed decentralized and runs on the mobile nodes themselves, requiring no central control. Experiments using ns2 simulator for up to 200 nodes show that the storage and bandwidth requirements of MVR grow slowly with the size of the network. Furthermore, MVR has demonstrated as self-administrating, fault-tolerant, and resilient under the different workloads. We also discuss alternative implementation options, and future work.

FDI location at the subnational level: a study of EJVs in China

The location of FDI activities by MNEs is of interest to international business researchers, especially in light of the rapidly changing economic landscapes in many regions of the world. This paper adds to the literature on MNEs' location choices, focusing on how business characteristics are related to location, in a sample of 6430 foreign equity joint ventures (EJVs) in China during 1984–1996. The results show that the duration of the EJV agreement, the origin of the foreign investor, and the type of business activity are related to the location of the EJVs' business activities within China. Significant differences are noted in the locations of ventures in the manufacturing and service sectors, and there is evidence of an increasing preference for MNEs to locate their activities in China's large, metropolitan cities. These findings reflect the dynamic nature of government policies toward FDI in China and their impact on the location choices of MNEs.

Application of an adaptive neural network with symbolic rule extraction to fault detection and diagnosis in a power generation plant

Artificial neural networks have a good potential to be employed for fault diagnosis and condition monitoring problems in complex processes. In this paper, the applicability of the fuzzy ARTMAP (FAM) neural network as an intelligent learning system for fault detection and diagnosis in a power generation plant is described. The process under scrutiny is the circulating water (CW) system, with specific attention to the conditions of heat transfer and tube blockage in the CW system. A series of experiments has been conducted systematically to investigate the effectiveness of FAM in fault detection and diagnosis tasks. In addition, a set of domain rules has been extracted from the trained FAM network so that its predictions can be explained and justified. The outcomes demonstrate the benefits of employing FAM as an intelligent fault detection and diagnosis tool with an explanatory capability for monitoring and diagnosing complex processes in power generation plants.

Condition monitoring and fault prediction via an adaptive neural network

This paper describes the application of an adaptive neural network, called Fuzzy ARTMAP (FAM), to handle fault prediction and condition monitoring problems in a power generation station. The FAM network, which is supplemented with a pruning algorithm, is used as a classifier to predict different machine conditions, in an off-line learning mode. The process under scrutiny in the power plant is the Circulating Water (CW) system, with prime attention to monitoring the heat transfer efficiency of the condensers. Several phases of experiments were conducted to investigate the `optimum' setting of a set of parameters of the FAM classifier for monitoring heat transfer conditions in the power plant.

Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM-CART model

In this paper, a novel approach to detect and classify comprehensive fault conditions of induction motors using a hybrid fuzzy min-max (FMM) neural network and classification and regression tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of real experiments is conducted, whereby the motor current signature analysis method is applied to form a database comprising stator current signatures under different motor conditions. The signal harmonics from the power spectral density are extracted as discriminative input features for fault detection and classification with FMM-CART. A comprehensive list of induction motor fault conditions, viz., broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems, has been successfully classified using FMM-CART with good accuracy rates. The results are comparable, if not better, than those reported in the literature. Useful explanatory rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors.