Adaptive Fault Diagnosis of Motors Using Comprehensive Learning Particle Swarm Optimizer with Fuzzy Petri Net

This study proposes and applies a comprehensive learning particle swarm optimization (CLPSO) fuzzy Petri net (FPN) algorithm, which is based on the CLPSO algorithm and FPN, to the fault diagnosis of a complex motor. First, the transition confidence is replaced by a Gaussian function to deal with the uncertainty of fault propagation. Then, according to the Petri net principle, a competition operator is introduced to improve the matrix reasoning. Finally, a CLPSO-FPN model for motor fault diagnosis is established based on the motor failure mechanism and fault characteristics. The CLPSO algorithm is used to generate the system parameters for fault diagnosis and to improve the adaptability and accuracy of fault diagnosis. This study considers the example of a three-phase asynchronous motor. The results show that the proposed algorithm can diagnose faults in this motor with satisfactory adaptability and accuracy compared with the traditional FPN algorithm. By establishing the system model, the fault propagation process of motors can be accurately and intuitively expressed, thus improving the fault treatment and equipment maintenance of motors

Enhancing Localization of Mobile Robots in Distributed Sensor Environments for Reliable Proximity Service Applications

Mobile robots can effectively coordinate information among sensor nodes in a distributed physical proximity. Accurately locating the mobile robots in such a distributed scenario is an essential requirement, such that the mobile robots can be instructed to coordinate with the appropriate sensor nodes. Packet loss is one of the prevailing issues on such wireless sensor network-based mobile robot localization applications. The packet loss might result from node failure, data transmission delay, and communication channel instability, which could significantly affect the transmission quality of the wireless signals. Such issues affect the localization accuracy of the mobile robot applications to an overwhelming margin, causing localization failures. To this end, this paper proposes an improved Unscented Kalman Filter-based localization algorithm to reduce the impacts of packet loss in the localization process. Rather than ignoring the missing measurements caused by packet loss, the proposed algorithm exploits the calculated measurement errors to estimate and compensate for the missing measurements. Some simulation experiments are conducted by subjecting the proposed algorithm with various packet loss rates, to evaluate its localization accuracy. The simulations demonstrate that the average localization error of the robot is 0.39 m when the packet loss rate is less than 90%, and the average running time of each iteration is 0.295 ms. The achieved results show that the proposed algorithm exhibits significant tolerance to packet loss while locating mobile robots in real-time, to achieve reliable localization accuracy and outperforms the existing UKF algorithm

Collaborative actuation of wireless sensor and actuator networks for the agriculture industry.

This paper investigates the deployment of collaborative estimation and actuation scheme of wireless sensor and actuator networks for the agriculture industry. In our proposed scheme, sensor nodes conduct a local estimation based on the Kalman filter for enhancing the estimation stability and further transmit data to the actuator nodes under a multi-rate transmission mode for enhancing the overall energy efficiency of the wireless network. Considering the mutual effect of related clusters, a collaborative actuation scheme of actuator nodes is integrated into our proposed scheme for improving the estimation accuracy and convergence speed. With an accurate estimation of the changes in the environmental parameters, combining the fuzzy neural network with the PID control algorithm, the actuator exerts reliable control over the environmental parameters. Performance evaluations and simulation analysis conducted based on the effects of temperature demonstrate the effectiveness of our proposed scheme in controlling the greenhouse environmental changes for in the agriculture industry.N/

Micro grid fault diagnosis based on redundant embedding Petri net

On account of the variable topology and multi-terminal power supply in micro-grid, the fault diagnosis faces more and more challenges. Traditional fault location criteria are unsuitable and fault diagnosis modelling is complex or poor versatility. Further on, the fault reasoning operation is time-consuming. A high transplantable fault diagnosis model aiming at the fault features in micro-grid is established in this paper, and a simple inference algorithm with good error-detecting capability is proposed. Firstly, the fault location criterion based on current magnitude, current phase and Distributed Generation’s current direction information is proposed, and the fault transient component is adopted as a supplementary criterion. Secondly, a hierarchical Petri net model utilizing the electrical information, relays’ and circuit breakers’ state information is accomplished. The model consists of fault location layer and fault clearance layer. In order to increase the portability of the model, the collective processing for the breakers is implemented. Moreover, ‘bidirectional arrowhead arc’ is introduced to reduce the number of places to optimize the Petri net model well. An improved redundant coding Petri net reasoning algorithm is proposed based on the fault clearance layer of the Petri net model. Finally, the validity of the method is verified through case analysis and comparison

Set-membership filtering for generator dynamic state estimation with delayed measurements

In this paper, the set-membership filtering problem is investigated for the dynamic state estimation (DSE) of the synchronous generator with delayed measurements. The process noise and measurement noise are assumed to be unknown, bounded and confined to a specified ellipsoidal set. The measurement delay is modeled by a special matrix composed of a delay-driven variable taking values of 1 or 0. Taking into explicit consideration the estimation uncertainty due to the linearization, the constrained Quasi-Newton method is adopted to minimize the linearization errors. The aim of this paper is to design a set-membership filter capable of confining the state estimate of the system to a certain ellipsoidal region, and the ellipsoidal set including all possible states is obtained by the convex optimization approach. Finally, the proposed algorithm is verified on a single machine infinite bus system to further demonstrate its effectiveness

A generalized alarm delay-timer’s performance indices computing method

With the rapid development of the modern process industry, the importance of the alarm system has become significant. In general, too many nuisance alarms exist in alarm systems and distract operators’ attention from paying attention to the real abnormal situation. As an effective technique to remove nuisance alarms, the alarm delay-timer is applied extensively in practice. Due to the defects of the alarm delay-timer, the generalized alarm delay-timer is proposed recently as an improvement. But the alarm performance indices alarm rate (FAR), missed alarm rate (MAR), and average alarm delay (AAD) for the generalized alarm delay-timer are not obtained easily so far. In view of this fact; first, a generalization computing method is proposed in the form of three formulas based on the Markov models. Second, the application range of the generalized alarm delay-timer and conventional alarm delay-timer are compared through a numerical simulation. Finally, the procedures of applying the generalized alarm delay-timer are illustrated by a simulation example

Three-dimensional personnel safety positioning based on improved UKF under complex coal mine environment

Aiming at the problems of strong interference and poor positioning accuracy in coal mines, this paper proposes a positioning algorithm for accurate detection of personnel safety. It is of great practical significance to detect the safety movement track of underground personnel. In this paper, WSNs distributed in coal mines are divided into several clusters by clustering method. Each cluster has a certain number of sensors, which can communicate with each other to keep the estimation consistency, and send the collected data to the cluster head (CH) node. System noise includes additive noise and multiplicative noise. In order to improve the accuracy of estimation, an improved UKF algorithm is proposed. The simulation results show that the improved UKF algorithm improves the accuracy and performance of estimation, and allows better location of the underground personnel