Nonstationary Current Signals Research Articles

Research on rotor fault diagnosis technology of three-phase asynchronous motor based on NA-MEMD mutual information and SVM

Aiming at the problem of mode aliasing in the adaptive decomposition of nonlinear and non-stationary current signals generated by three-phase asynchronous motor faults, and the fault features contained in signals collected by a single sensor can not be accurately and comprehensively extracted and characterized when early rotor bar breakage and air gap eccentricity faults occur, A fault diagnosis method for three-phase asynchronous motor based on noise assisted multivariate empirical mode decomposition (NA-MEMD) and mutual information is proposed. Firstly, the NA-MEMD algorithm is used to decompose the three-phase stator current signal of the asynchronous motor to obtain multi-scale intrinsic mode functions (IMFs). Then, the correlation algorithm is used to screen the IMFs containing useful information. Then, the filtered IMF components are reconstructed into new signals and their features are extracted, Finally, support vector machines (SVM) are used to identify the rotor broken bars and air gap eccentric faults of the three-phase asynchronous motor. The experimental results show that the NA-MEMD method has a higher recognition rate than the traditional empirical mode decomposition (EMD) and the ensemble empirical mode decomposition (EEMD) methods.

Modified Fuzzy-Q-Learning (MFQL)-Based Mechanical Fault Diagnosis for Direct-Drive Wind Turbines Using Electrical Signals

In this paper, a self-learning multi-class intelligent model for wind turbine fault diagnosis is proposed by using MFQL (Modified-Fuzzy-Q-Learning) technique. The MFQL is adaptive in nature and extension of fuzzy-Q-learning method where look-up table of Q-learning is conquered by fuzzy based approximation strategy to reduce the curse of dimensionality of the Q-learning. The proposed MFQL classifier diagnoses the mechanical and imbalance faults without using mechanical sensors. Proposed methodology is addressed with relying on PMSG (Permanent Magnet Synchronous Generator) stator current signals, which is already being used by protection system of wind turbines. According to the aforementioned description, non-stationary current signals of PMSG have been pre-processed to extract the input features by empirical mode decomposition followed with J48 algorithm based most relevant input feature selection. For the one-step ahead performance demonstration of the proposed MFQL approach, results have been compared with neural network, support vector machines, fuzzy logic, and conventional Fuzzy-Q-Learning techniques. Demonstrated results outperform the capability of proposed MFQL approach. Moreover, MFQL is developed first time to implement in the area of WTGS fault diagnosis in the literature.

Tool Wear Condition Monitoring by Combining Variational Mode Decomposition and Ensemble Learning.

Most online tool condition monitoring (TCM) methods easily cause machining interference. To solve this problem, we propose a method based on the analysis of the spindle motor current signal of a machine tool. Firstly, cutting experiments under multi-conditions were carried out at a Fanuc vertical machining center, using the Fanuc Servo Guide software to obtain the spindle motor current data of the built-in current sensor of the machine tool, which can not only apply to the actual processing conditions but, also, save costs. Secondly, we propose the variational mode decomposition (VMD) algorithm for feature extraction, which can describe the tool conditions under different cutting conditions due to its excellent performance in processing the nonstationary current signal. In contrast with the popular wavelet packet decomposition (WPD) method, the VMD method was verified as a more effective signal-processing technique according to the experimental results. Thirdly, the most indicative features that relate to the tool condition were fed into the ensemble learning (EL) classifier to establish a nonlinear mapping relationship between the features and the tool wear level. Compared with existing TCM methods based on current sensor signals, the operation process and experimental results show that using the proposed method for the monitoring signal acquisition is suitable for the actual processing conditions, and the established tool wear prediction model has better performance in both accuracy and robustness due to its good generalization capability.

Fault Diagnosis of Ball Screw in Industrial Robots Using Non-Stationary Motor Current Signals

With the advancement of intelligent manufacturing, different kinds of industrial robots have been applied in modern factories. The liquid crystal display transfer robot (LCDTR) has been widely used in LCD production lines to transport panels. Effective fault diagnosis and prognosis of the industrial robots are of great importance, since unplanned downtime caused by faulty robots significantly reduces the production capacity. Specifically, the ball screw is the critical component in the LCDTR. The failure of the ball screw can cause long downtime. Conventionally, the fault diagnosis of the ball screw is usually based on the vibration signals. However, it is extremely difficult to install the vibration sensors in the industrial robots. Therefore, in order to address this issue in condition monitoring, this paper proposes a data-driven fault diagnosis methodology using the motor current signals of the ball screw. Two time-frequency domain analysis methods are investigated, including short-time Fourier transform (STFT) and wavelet packet decomposition (WPD). The statistical features are extracted, and Fisher score is used to select features. Furthermore, the logistic regression and k-nearest neighbors are applied for the final fault diagnosis. Experiments on a real-world industrial robot dataset are carried out for validation. 100% diagnosis accuracy can be basically achieved by the proposed method, which indicates the non-stationary current signal can be effectively used to identify the health states of the ball screw in the LCDTR.

Characterization of cavitation and seal damage during pump operation by vibration and motor current signal spectra

Cyclic spectral analysis is conducted to extract the frequency characteristic components from nonstationary current signals under cavitation and seal damage conditions as indicators of fault characterization. Specifically, vibration and current signals are both acquired and pre-processed by envelope extraction and singular value decomposition methods, respectively. Since there are more noise waves in vibration power spectral density spectra in comparison with current signals, the cyclic spectral analysis is conducted for the current signal analysis. Cyclic autocorrelation functions of signals are firstly calculated then corresponding slices are extracted from the cyclic autocorrelation functions and processed by fast Fourier transform method. Consequently from the spectra, indicators for characterization are extracted to demonstrate the occurrence and severity of faults. Results show that the rotating frequency component shows obvious tendency as the flow rate deviates from the designed point without any faults. The indicators for seal damage characterization are 250 Hz and 350 Hz frequency components related with rotating frequency and blade passing frequency in the current signals. Meanwhile, the occurrence and development status of cavitation are indicated by the frequency component of 100 Hz in the slice spectra of cyclic autocorrelation functions. Additionally, the effect of instability caused by off-design, seal damage, as well as cavitation operation conditions is distinguished by cyclic spectral analysis. Characteristic components are innovatively extracted at specific frequencies to avoid the effect from modulated and noise components in the current signals. And the results about the recognition of different types of faults could provide important evidences for fault detection during pump operation.

A Novel Technique Using Multiresolution Wavelet Packet Decomposition for Real Time Diagnosis of Hunting in Line Start IPM Motor Drives

A line-start interior permanent magnet (LSIPM) motor operates with inherent hunting phenomena. This paper presents a novel procedure for real-time diagnosis of hunting in LSIPM motors based on stator current signatures. Torsional vibration associated with hunting in a LSIPM motor influences the electrical supply, and introduces variable amplitude lower and upper sidebands in the stator current. In this paper, wavelet packet decomposition is applied to perform multiresolution analysis of the nonstationary current signals for detection and extraction of the sideband frequency components superimposed on the fundamental. Statistical techniques are applied on the extracted data signals and a signature for the diagnosis of hunting phenomenon is established. The proposed technique is validated by performing finite element analysis (FEA) of two three-phase four-pole LSIPM motors of different power ratings for various case scenarios. Experimental investigations have been carried out for a three-phase four-pole 208-V, 1-HP LSIPM motor drive in order to validate the performance of the proposed method under various operating conditions. Based on FEA and experimental results, the proposed technique can successfully diagnose the hunting phenomenon without a vibration sensor.

Quantitative Evaluation of Wind Turbine Faults Under Variable Operational Conditions

Wind turbines have been widely used for clean and renewable electricity generation. The maintenance costs of wind turbines constitute a significant portion of the total cost of the generated electricity. Thus, health management systems are increasingly needed to reduce the maintenance costs and improve the reliability of wind turbines. This paper proposes a novel framework for the quantitative evaluation of faults and health conditions of wind turbines using generator current signals. A synchronous resampling algorithm is designed to handle nonstationary current signals for fault feature extraction. The extracted fault features are used to reconstruct new signals, whose correlation dimensions are then calculated by using the Grassberger–Procaccia (G–P) algorithm for fault and health condition evaluation of the wind turbines. Experimental studies are carried out for a direct-drive wind turbine equipped with a permanent-magnet synchronous generator (PMSG) in the healthy condition and two faulty conditions. Results show that the proposed framework can not only detect the faults but also quantify different health conditions for the wind turbine.

Fast Fourier and discrete wavelet transforms applied to sensorless vector control induction motor for rotor bar faults diagnosis

This paper presents new techniques to evaluate faults in case of broken rotor bars of induction motors. Procedures are applied with closed-loop control. Electrical and mechanical variables are treated using fast Fourier transform (FFT), and discrete wavelet transform (DWT) at start-up and steady state. The wavelet transform has proven to be an excellent mathematical tool for the detection of the faults particularly broken rotor bars type. As a performance, DWT can provide a local representation of the non-stationary current signals for the healthy machine and with fault. For sensorless control, a Luenberger observer is applied; the estimation rotor speed is analyzed; the effect of the faults in the speed pulsation is compensated; a quadratic current appears and used for fault detection.

Prognostics for drilling process with wavelet packet decomposition

On-line tool condition monitoring is highly needed in drilling production process. Input current has been employed to monitor the drilling tool wear by many researchers. But few cases can represent the wear status and recognize the breakage simultaneously. The remaining life of tool has not been discussed sufficiently. This paper presents a strategy of on-line tool monitoring system for drilling machine using wavelet packet decomposition of spindle current signature. A moving window technique is used to extract the real drilling parts of data from sampled data sequence. The wavelet packet decomposition is used to extract features from non-stationary current signal. Critical features are selected according to their ability of discriminating the wear progress under Fisher criterion. Logistic regression combined with autoregressive moving average models are used to evaluate the failure possibility and remaining life of the drill bit. Experimental results show good performance of the proposed algorithm.

An Induction Motor Speed Measurement Method Based on Current Harmonic Analysis With the Chirp-Z Transform

This paper presents a new method to measure motor speed by means of frequency estimation of rotor slot spectral components in the supply current of squirrel single-cage induction motors. The novelty of the method consists in the harmonic analysis of the supply current by means of the chirp-Z transform (CZT). The advantages are improved accuracy due to better spectral resolution and resolvability. Moreover, a shorter observation window is required, thus reducing errors related to nonstationary current signals. The experimental results are presented to validate the proposed method and to make a comparison with a similar method based on the fast Fourier transform (FFT).