Condition monitoring

About: Condition monitoring is a research topic. Over the lifetime, 13911 publications have been published within this topic receiving 201649 citations.

Localized Structural Health Monitoring Using Energy-Efficient Wireless Sensor Networks

Abstract: This paper presents a localized information processing approach for long-term, online structural health monitoring (SHM) using wireless sensor networks (WSNs). Based on the embedded AR-ARX method, each sensor independently calculates a statistical damage-sensitive coefficient using the measured acceleration data during each monitoring period. A nonlinear programming formulation is developed to identify damage presence, localize damage position, and quantify damage severity from the damage-sensitive coefficients in the whole sensing field. By limiting each sensor to exchange information among its neighboring sensors only, a localized near-optimal algorithm is proposed to reduce communication costs, thus alleviating the channel interference and prolonging the network lifetime. Simulation results on a steel frame structure prove the effectiveness of the proposed algorithm.

A decision fusion algorithm for tool wear condition monitoring in drilling

Abstract: Tool wear monitoring of cutting tools is important for the automation of modern manufacturing systems. In this paper, several innovative monitoring methods for on-line tool wear condition monitoring in drilling operations are presented. Drilling is one of the most widely used manufacturing operations and monitoring techniques using measurements of force signals (thrust and torque) and power signals (spindle and servo) are developed in this paper. Two methods using Hidden Markov models, as well as several other methods that directly use force and power data are used to establish the health of a drilling tool in order to avoid catastrophic failure of the drill. In order to increase the reliability of these methods, a decision fusion center algorithm (DFCA) is proposed which combines the outputs of the individual methods to make a global decision about the wear status of the drill. Experimental results demonstrate the effectiveness of the proposed monitoring methods and the DFCA.

Rational-Dilation Wavelet Transform Based Torque Estimation from Acoustic Signals for Fault Diagnosis in a Three-Phase Induction Motor

Abstract: Condition monitoring of electric drives play a significant role for a safe working environment. Induction motors are widely used in industries and any fault in it leads to interruption in the process or complete shutdown of the equipment. In this paper, acoustic-based fault detection in a three-phase induction motor is done by estimating the torque from the acoustic signals released by the machine. The fault detection is possible as acoustic emission is different for faults such as single phasing, bearing cage damage, and broken rotor bars. The acoustic signals are processed using rational-dilation wavelet transform (RADWT) technique to extract the fault features and thereby diagnose the fault type. The torque estimation is done using multiple regression method by extracting the energy possessed in the processed acoustic signal and the faults are diagnosed precisely. An experimental setup comprising of a three-phase induction motor with brake drum loading is used to validate this approach. The RADWT has adjustable frequency resolution in comparison with other wavelet methods. When high Q-factor filters are employed in the RADWT, better representation of different faults are obtained in the decomposed sub-bands. In addition, characteristic frequencies of different faults are calculated analytically and validated by observing the frequencies in the FFT spectrum of acoustic fault signals.

Monitoring of induction machines by maximum covariance method for frequency tracking

Abstract: Motor current signature analysis (MCSA) has been widely investigated in order to monitor fault conditions of induction machines. On the other hand several solutions were proposed for the detection of rotor speed of induction motor for sensorless control. Another deeply investigated field of research is the detection of supply frequency of power lines, for the diagnosis of the distribution network. A common root of these three key topics is the need of accurately stating specific spectrum frequencies. Several techniques were presented in the literature in order to perform accurate tracking of frequencies for different purposes. They are modified versions of the traditional discrete Fourier transformation (DFT), or novel spectrum estimation techniques. This paper presents a novel procedure based on the statistical analysis of the current signal in the time domain, referred to as maximum covariance method for frequency tracking (MCMFT), that allows to obtain high frequency resolution accuracy independently of the sampling frequency and of the time acquisition period. Therefore those spectrum lines related to supply frequency or to slip can be detected with extreme accuracy within a wide range of sampled data conditions. Then either an accurate diagnosis of the machine electric faults or sensorless control, or distribution network diagnosis can be performed. Comparison between the proposed method and the literature are reported, in order to critically analyze its performances. An induction machine with two artificially broken bars was used for the experiments.