Fuzzy Reasoning Numerical Spiking Neural P Systems for Induction Motor Fault Diagnosis.
Xiu Yin1, Xiyu Liu1, Minghe Sun2
1Academy of Management Science, Business School, Shandong Normal University, Jinan 250014, China.
Entropy (Basel, Switzerland)
|July 8, 2023
Summary
Fuzzy reasoning numerical spiking neural P systems effectively diagnose induction motor faults using interval-valued fuzzy numbers. This approach handles uncertain fault data, offering advantages over existing diagnostic methods.
Area of Science:
- Computational Intelligence
- Artificial Intelligence
- Engineering
Background:
- Numerical spiking neural P systems (NSN P systems) are computational models inspired by biological neurons.
- Existing methods for induction motor fault diagnosis struggle with incomplete and uncertain data.
Purpose of the Study:
- To introduce fuzzy reasoning numerical spiking neural P systems (FRNSN P systems) for improved induction motor fault diagnosis.
- To model fuzzy production rules and perform fuzzy reasoning for motor fault identification.
Main Methods:
- Developed FRNSN P systems by integrating interval-valued triangular fuzzy numbers into NSN P systems.
- Designed a FRNSN P reasoning algorithm to handle uncertain fault information.
- Utilized relative preference relationships to assess fault severity.
Main Results:
- FRNSN P systems successfully modeled fuzzy production rules for motor faults.
- The FRNSN P reasoning algorithm effectively diagnosed single and multiple induction motor faults.
- The proposed method demonstrated advantages over existing fault diagnosis techniques.
Conclusions:
- FRNSN P systems provide a robust framework for induction motor fault diagnosis.
- The use of interval-valued triangular fuzzy numbers enhances the handling of uncertain fault data.
- The developed algorithm offers a promising alternative for timely motor maintenance and repair.
Related Concept Videos
01:07Time-Domain Interpretation of PD Control
144
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
Consider the example of control of motor torque. Initially, a positive...
144
01:21Power System Three-Phase Short Circuits
114
Determining the subtransient fault current in a power system involves representing transformers by their leakage reactances, transmission lines by their equivalent series reactances, and synchronous machines as constant voltage sources behind their subtransient reactances. In this analysis, certain elements are excluded, such as winding resistances, series resistances, shunt admittances, delta-Y phase shifts, armature resistance, saturation, saliency, non-rotating impedance loads, and small...
114
01:19Electro-mechanical Systems
1.0K
Electromechanical systems are intricate configurations that effectively combine electrical and mechanical elements to achieve a desired outcome. Central to many of these systems is the DC motor, a device that converts electrical energy into mechanical motion, enabling various applications ranging from simple fans to complex robotic mechanisms.
A key component of the DC motor is the armature, a rotating circuit positioned within a magnetic field. As an electric current passes through the...
A key component of the DC motor is the armature, a rotating circuit positioned within a magnetic field. As an electric current passes through the...
1.0K
01:25Multimachine Stability
197
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
197
01:21Three-Phase Short Circuit—Unloaded Synchronous Machine
169
Conducting a three-phase short circuit test on an unloaded synchronous machine helps understand its impact on the system. The AC fault current's oscillogram, with the DC offset removed, reveals that the waveform amplitude decreases from an initially high value to a steady-state level for one phase of the machine.
This behavior occurs due to the magnetic flux produced by the short-circuit armature currents. Initially, these currents follow high-reluctance paths but eventually shift to...
This behavior occurs due to the magnetic flux produced by the short-circuit armature currents. Initially, these currents follow high-reluctance paths but eventually shift to...
169
01:24Bus Impedance Matrix
149
Calculating subtransient fault currents for three-phase faults in an N-bus power system involves using the positive-sequence network. When a three-phase short circuit occurs at a specific bus, the analysis uses the superposition method to evaluate two separate circuits.
In the first circuit, all machine voltage sources are short-circuited, leaving only the prefault voltage source at the fault location. The positive-sequence bus impedance matrix can be determined by solving the nodal equations,...
In the first circuit, all machine voltage sources are short-circuited, leaving only the prefault voltage source at the fault location. The positive-sequence bus impedance matrix can be determined by solving the nodal equations,...
149