A Wireless Sensor System for Real-Time Monitoring and Fault Detection of Motor Arrays

Abstract

This paper presents a wireless fault detection system for industrial motors that combines vibration, motor current and temperature analysis, thus improving the detection of mechanical faults. The design also considers the time of detection and further possible actions, which are also important for the early detection of possible malfunctions, and thus for avoiding irreversible damage to the motor. The remote motor condition monitoring is implemented through a wireless sensor network (WSN) based on the IEEE 802.15.4 standard. The deployed network uses the beacon-enabled mode to synchronize several sensor nodes with the coordinator node, and the guaranteed time slot mechanism provides data monitoring with a predetermined latency. A graphic user interface offers remote access to motor conditions and real-time monitoring of several parameters. The developed wireless sensor node exhibits very low power consumption since it has been optimized both in terms of hardware and software. The result is a low cost, highly reliable and compact design, achieving a high degree of autonomy of more than two years with just one 3.3 V/2600 mAh battery. Laboratory and field tests confirm the feasibility of the wireless system.

Resilient Model Predictive Control (RMPC) Technique Based Induction Motor Monitoring and Control using Labview

“…Be that as it may, remote innovation contrasts from multiple points of view from wire. These distinctions present huge system models, convention configuration difficulties, and devices for mechanical and computerization applications [16][17][18]. Numerous gadgets and machines can be controlled and information isn't gotten and can be transmitted by remote innovation simultaneously.…”

Distributed Fault Detection for Wireless Sensor Networks Based on Support Vector Regression

“…In recent years, many scholars have proposed a number of improved distributed fault detection methods based on the DFD algorithm [18][19][20][21][22][23][24][25][26][27]. References [28][29][30][31][32] show that when the number of neighboring network nodes is small and the probability of node failure is large, the DFD algorithm performance will decrease sharply because the DFD algorithm judges too harshly if the node is normal condition. Therefore, [32] improved the DFD algorithm and modified the judgment conditions of the final state of DFD algorithm.…”

Fault Diagnosis of Induction Machines in Transient Regime Using Current Sensors with an Optimized Slepian Window

“…From (6) it can be seen that the analysis window has a major effect in the spectrogram of the current. It highlights the harmonic components of the current, but, at the same time, it smears the spectrogram (6), so it has a major impact in the reliability of the fault diagnostic procedure. The election of a window maximally confined to a region of the TF plane with a limited duration and bandwidth is crucial to obtain a high resolution spectrogram, which accurately reflects the fault components of the current in the TF plane, with a minimum of the smearing due to use of the window.…”

“…Thus, the early detection of induction machine (IM) faults and the machine condition prognosis are crucial to reduce maintenance costs [2] and to avoid costly, unexpected shut-downs [3]. Fault diagnosis via the current analysis in the frequency domain has become a common method for machine condition evaluation because it is non-invasive, it requires a single current sensor, either a current transformer, a Hall sensor, or a magnetoelectric current sensor [4], and it can identify a wide variety of machine faults [5,6]. Traditionally, these techniques, known as motor current signature analysis (MCSA), have focused on the detection of faults during the steady state functioning of the machine through the current spectrum, which can be computed using the fast Fourier transform (FFT) [7][8][9][10].…”