Design With Operational Amplifiers And Analog Integrated Circuits

Rolling element bearings are one of the most critical components of rotating machinery, with bearing faults amounting up to 50% of the faults in electrical machines. Therefore, the bearing fault diagnosis has attracted attention of many researchers. Typically, the bearing fault diagnosis is performed using vibration signals from the machine. In addition, by using deep learning algorithms on the vibration signals, the fault detection accuracy close to 100% can be achieved. However, measurement of vibration signals requires an additional sensor, which is not present in majority of the machines. Nevertheless, with an alternative approach, the stator current can be used for diagnosis. Therefore, the paper emphasizes on the diagnosis of bearing faults using the stator current. The diagnosis requires signal processing for the fault signature extraction that is buried underneath the noise in the current signal. The paper uses the Paderborn University damaged bearing dataset, which contains stator current data from healthy, real damaged inner raceway and real damaged outer raceway bearings with different fault severity. For fault signature extraction from the current signals, the redundant frequencies in the signals are filtered, then from the filtered signals eight features are extracted from the time and time-frequency domain by using the wavelet packet decomposition (WPD). Then, using these features and the well known deep learning algorithm Long Short-Term Memory (LSTM), bearing fault classification is made. The deep learning LSTM algorithm is mostly used in speech recognition due to its time coherence, but in this paper, the ability of LSTM is also demonstrated with the fault classification accuracy of 96%, which outperforms most of the present algorithms that perform bearing fault diagnosis using stator current. The method developed is independent of the speed and the loading conditions.

LSTM Based Bearing Fault Diagnosis of Electrical Machines using Motor Current Signal

The paper describes the modeling and implementation of overcurrent relay with different overcurrent characteristics using MATLAB/Simulink. A Simulink block with generic design of overcurrent relay is designed incorporating IEC and IEEE standards and is ready to be used for any power system protection scheme. Consequently, the performance of the Simulink block is validated and IDMT Standard inverse characteristic curve is obtained for different values of Time Multiplier Settings and Plug Setting Multipliers. Furthermore the performance of software model is tested on radial feeder protection, simulated in Simulink environment. In addition to that a user-friendly Graphical User Interface is designed in way that user can easily operate radial feeder protection scheme by setting relays at different parameters and check the performance and reliability of the network for different fault locations. The overall effort focusses in teaching and improving learning experiences of student undertaking Power System Protection course. Moreover this software block of digital relay can be tested on any real time protection scheme involving IDMT overcurrent relay, within Simulink Environment.

Modelling of overcurrent relay with inverse characteristics for radial feeder protection using graphical user interface

Many industrial applications use the induction machine for its advantages, like robustness. But like any other machines, it can be affected by several faults such as broken rotor bars, stator inter-turn short-circuit, bearing faults, etc. On the one hand, mechanical faults produce vibration, eccentricity and torque oscillations which influence the stator current and the distribution of the magnetic field. Therefore, early detection of mechanical faults leads to avoid damage or sudden stop of the induction machine. In this context, this paper studies the performances of three fault detection and diagnostic techniques for rolling bearing failures. The first technique is based on the stator current analysis, the second one uses the vibration signal analysis and the last technique is devoted to the stray flux signature.The aim of the study is to highlight the performances of stray- flux technique in the detection of inner raceway fault comparatively to current and vibration. For this study, experimental tests are realized on a laboratory test bench allowing creation of artificial bearing damage. The analysis is focused on specific harmonics related to the electrical and mechanical frequencies.

Rolling Bearing Failure Detection in Induction Motors using Stator Current, Vibration and Stray Flux Analysis Techniques

Nowadays, the monitoring of rotatory machinery is an essential task in the industry, due to its help in specific maintenance programs, increasing the life of the machine, reducing interventions, and getting off costs in spare parts. The bearings are essential pieces of rotatory machinery; thus, monitoring and fault detection are important tasks to maintain their proper functioning. In this paper, a methodology for bearing fault detection on induction motors based on Motor Current Signature Analysis, the Sparse Representation, and Dictionary Learning techniques are presented. The fault detection is possible due to the Sparse Representation is developed with dictionaries trained with specific faults each one, obtaining a minor error with the dictionary related to the same fault of the analyzed signal. In this way, the detection of related to localized defects and distributed defects are possible. Bearings of three-phase and 1-HP induction motors with two load conditions were used to testing. The proposed methodology presents an accuracy greater than 96%.

Bearing Fault Detection on IM Using MCSA and Sparse Representation

This paper presents the results of laboratory work carried out for identifying the outer race bearing fault occurred in an induction motor. The knowledge about fault behavior of an induction motor is extremely important for overall operational life of the machine. The paper refers to spectral analysis of the motor stator current, which includes routine stator current, noise and specific fault current signature. Using separate healthy and faulty bearing on the machine, testing is carried out for obtaining set of healthy and faulty currents for different load conditions. The specific fault signature can be separated using feature extraction in time domain Wavelet and after getting spectral information using classification technique ANN fault is identified.

Outer race bearing fault identification of induction motor based on stator current signature by wavelet transform

Power Systems suffer from various types of faults such as Phase and Earth Faults These faults lead to overcurrent in the circuits Numerical Relays embedded with Digital Signal Processor (DSP) can be used to protect the system components as they can perform complex processing faster with higher accuracy and also avoid problem of false tripping DSP based processing can also be optimized for real time In this paper, TMS320F28335 based Implementation of Numerical Relay is discussed TMS320F28335 is a floating point Digital Signal Controller (DSC) Short Time Fourier Transform (STFT) is used to detect the spurious signals and faults more accurately The proposed overcurrent protection system is implemented on real time basis with the help of AntiAliasing filter and Signal Conditioning Circuit to scale the inputs from Current Transformer Anti-Aliasing filter is designed based on the fault current simulation data generated using Power System Simulation Software Fault current patterns generated are analyzed by calculating Fourier Transform coefficients that decide the filter specifications Code Composer Studio (CCS) IDE is used for the development of algorithm The paper discusses development of overcurrent protection algorithm based on RMS calculation for single phase circuits using TMS320F28335 The overcurrent relay Inverse Definite Minimum Time (IDMT) characteristics curves based on the simulated fault current data are used as basis and are implemented for Single Phase Circuits

TMS320F28335 based single phase overcurrent protection Implementation using Numerical Relay

Power Systems suffer from various types of faults such as Phase and Earth Faults. These faults lead to overcurrent in the circuits. Numerical Relays embedded with Digital Signal Processor (DSP) can be used to protect the system components as they can perform complex processing faster with higher accuracy and also avoid problem of false tripping. DSP based processing can also be optimized for real time. In this paper, TMS320F28335 based Implementation of Numerical Relay is discussed. TMS320F28335 is a floating point Digital Signal Controller (DSC). Short Time Fourier Transform (STFT) is used to detect the spurious signals and faults more accurately. The proposed overcurrent protection system is implemented on real time basis with the help of AntiAliasing filter and Signal Conditioning Circuit to scale the inputs from Current Transformer. Anti-Aliasing filter is designed based on the fault current simulation data generated using Power System Simulation Software. Fault current patterns generated are analyzed by calculating Fourier Transform coefficients that decide the filter specifications. Code Composer Studio (CCS) IDE is used for the development of algorithm. The paper discusses development of overcurrent protection algorithm based on RMS calculation for single phase circuits using TMS320F28335. The overcurrent relay Inverse Definite Minimum Time (IDMT) characteristics curves based on the simulated fault current data are used as basis and are implemented for Single Phase Circuits.

Paper presents outer race bearing fault detection system in an induction motor based on nonlinear decomposition and Time frequency representation. Overall life of any machine is based on its timely identification of fault scenario and the appropriate corrective action taken for it. The paper referrers to timely identification of bearing fault behavior of an induction motor using stator current analysis. It is the combination of regular stator current, noise and required component of faulty bearing signature. The fault signature is carried out by comparing a set of two bearings mainly healthy and faulty bearing on the same machine. Testing is carried out for obtaining dataset of currents for healthy and faulty bearing operated for different load conditions. This detection is inscrutable as change in the obtained current wave is very small for different energy levels as well as frequency. Separating noise pattern and fault pattern is critical. Using Hilbert Vibration Decomposition (HVD) method Current wave is decomposed into number of mono components keeping phase information intact. For accelerating further process of identification, time information of decomposed components should be unchanged. Smoothed Pseudo Wigner-Ville distribution (SPWVD) with reassignment is applied to decomposed components for localizing fault zone in current wave. As a next step, feature extraction is followed by localization of fault part. At last training is obtained for the features extracted from the wave and for this process, Support Vector Machine (SVM) classifier is used for training dataset. In testing phase the system gives binary output i.e. faulty or non-faulty bearing. The result analysis shows that the proposed method can detect bearing fault correctly. Performance analysis of proposed method demonstrates accuracy rate of as high as 95%. Keywords—Induction motor, stator current signature, Hilbert Vibration Decomposition (HVD), Smoothed Pseudo Wigner-Ville distribution (SPWVD), Support vector Machine (SVM)

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Saket Yeolekar

Papers

Outer race bearing fault identification of induction motor based on stator current signature by wavelet transform

TMS320F28335 based single phase overcurrent protection Implementation using Numerical Relay

TMS320F28335 based single phase overcurrent protection Implementation using Numerical Relay

Outer Race Bearing Fault Identification of Induction Motor based on Nonlinear Signal Detection and Time Frequency Representation