A review on machinery diagnostics and prognostics implementing condition-based maintenance

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Wireless sensor and actor networks (WSANs) refer to a group of sensors and actors linked by wireless medium to perform distributed sensing and acting tasks. The realization of wireless sensor and actor networks (WSANs) needs to satisfy the requirements introduced by the coexistence of sensors and actors. In WSANs, sensors gather information about the physical world, while actors take decisions and then perform appropriate actions upon the environment, which allows a user to effectively sense and act from a distance. In order to provide effective sensing and acting, coordination mechanisms are required among sensors and actors. Moreover, to perform right and timely actions, sensor data must be valid at the time of acting. This paper explores sensor-actor and actor-actor coordination and describes research challenges for coordination and communication problems.

Wireless sensor and actor networks: research challenges

Deep learning and its applications to machine health monitoring

Control methodologies in networked control systems

Motor systems are very important in modern society. They convert almost 60% of the electricity produced in the US into other forms of energy to provide power to other equipment. In the performance of all motor systems, bearings play an important role. Many problems arising in motor operations are linked to bearing faults. In many cases, the accuracy of the instruments and devices used to monitor and control the motor system is highly dependent on the dynamic performance of the motor bearings. Thus, fault diagnosis of a motor system is inseparably related to the diagnosis of the bearing assembly. In this paper, bearing vibration frequency features are discussed for motor bearing fault diagnosis. This paper then presents an approach for motor rolling bearing fault diagnosis using neural networks and time/frequency-domain bearing vibration analysis. Vibration simulation is used to assist in the design of various motor rolling bearing fault diagnosis strategies. Both simulation and real-world testing results obtained indicate that neural networks can be effective agents in the diagnosis of various motor bearing faults through the measurement and interpretation of motor bearing vibration signatures.

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Neural-network-based motor rolling bearing fault diagnosis

There are two general structures to design a control system through a network. The first structure is to have several subsystems, in which each of the subsystem contains a set of sensors, a set of actuators, and a controller by itself. These system components are attached to the same control plant. In this case, a subsystem controller receives a set point from the central controller. Another structure is to connect a set of sensors and a set of actuators to a network directly. Sensors and actuators in this case are attached to a plant, while a controller is separated from the plant via a network connection to perform a closed-loop control over the network. A challenging problem in control of networked-based system is network delay effects. The time to read a sensor measurement and to send a control signal to an actuator through the network depends on network characteristics such as their topologies, routing schemes, etc. Therefore, the overall performance of a network-based control system can be significantly affected by network delays. The severity of the delay problem is aggravated when data loss occurs during a transmission. Moreover, the delays do not only degrade the performance of a network-based control system, but also can destabilize the system. This tutorial presents fundamental details of network-based control and recent network-based control techniques for handling the network delays. The techniques are based on various concepts such as state augmentation, queuing and probability theory, nonlinear control and perturbation theory, and scheduling. A general structure of a network-based control system, delay types, and delay behaviors are also described in this tutorial. In addition, advantages and disadvantages of these techniques are discussed.

Network-based control systems: a tutorial

Conventionally, in order to control an application over a data network, a specific networked control or teleoperation algorithm to compensate network delay effects is usually required for controller design. Therefore, an existing controller has to be redesigned or replaced by a new controller system. This replacement process is usually costly, inconvenient, and time consuming. In this paper, a novel methodology to enable existing controllers for networked control and teleoperation by middleware is introduced. The proposed methodology uses middleware to modify the output of an existing controller based on a gain scheduling algorithm with respect to the current network traffic conditions. Since the existing controller can still be utilized, this approach could save much time and investment cost. Two examples of the middleware applied for networked control and teleoperation with IP network delays are given in these two companion papers. Part I of these two companion papers introduces the concept of the proposed middleware approach. Formulation, delay modeling, and optimal gain finding based on a cost function for a case study on DC motor speed control with a proportional-integral (PI) controller are also described. Simulation results of the PI controller shows that, with the existence of IP network delays, the middleware can effectively maintain the networked control system performance and stabilize the system. Part II of this paper will cover the use of the proposed middleware concept for a mobile robot teleoperation.

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Gain scheduler middleware: a methodology to enable existing controllers for networked control and teleoperation - part I: networked control

Connecting a complex control system with various sensors, actuators, and controllers as a networked control system by a shared data network can effectively reduce complicated wiring connections. This system is also easy to install and maintain. The trend is to use networked control systems for time-sensitive applications, such as remote DC motor actuation control. The performance of a networked control system can be improved if the network can guarantee quality-of-service (QoS). Due to time-varying network traffic demands and disturbances, QoS requirements provided by a network may change. In this case, a network has to reallocate its resources and may not be able to provide QoS requirements to a networked control application as needed. Therefore, the application may have to gracefully degrade its performance and perform the task as best as possible with the provided network QoS. This paper proposes a novel approach for networked DC motor control systems using controller gain adaptation to compensate for the changes in QoS requirements. Numerical and experimental simulations, and prototyping, are presented to demonstrate the feasibility of the proposed adaptation scheme to handle network QoS variation in a control loop. The effective results show the promising future of the use of gain adaptation in networked control applications.

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Y. Tipsuwan

Papers

Control methodologies in networked control systems

Neural-network-based motor rolling bearing fault diagnosis

Network-based control systems: a tutorial

Gain scheduler middleware: a methodology to enable existing controllers for networked control and teleoperation - part I: networked control

Gain adaptation of networked DC motor controllers based on QoS variations