Transient Analysis and Motor Fault Detection using the Wavelet Transform

This paper demonstrates the development of a flexible fault diagnosis methodology that can detect up to ten different faults in the induction motor (IM), simultaneously. The major IM electrical faults, such as the broken rotor bar (BRB), phase unbalance (PUF), and stator winding fault (SWF), and mechanical faults, such as bearing fault (BF), unbalanced rotor (UR), bowed rotor (BR), and misaligned rotor (MR), are considered with different fault severities for the diagnosis. The experiments are conducted with three varying loads and seven different speeds, and the frequency domain vibration and current data are acquired at a relatively low sampling rate of 1 kHz. Several statistical features are extracted and then the best feature-set is selected using the wrapper model. Thereafter, a data classification tool based on the support vector machine (SVM) is used for the fault characterization. Initially, a multi-fault diagnosis is performed by training and testing the SVM at the same operating conditions (i.e., load and speed). The performance of the classifier is found to be very good at all IM operating conditions. The main focus of this study lies in overcoming the fault diagnosis, where the data are unavailable at required operating conditions. This is accomplished by employing interpolation and extrapolation strategies for different loads and speeds. The proposed methodology not only solves practical problem of unavailability of data at different operating conditions but also shows good performance and takes low computation time, which are vital requirements of an online intelligent condition monitoring system.

Multiphase induction machines have gained more attentions due to their superior fault tolerant capabilities. This work aims to assess the Broken Rotor Bar (BRB) faulty operation of asymmetrical six-phase Induction Machine (IM). Additionally, a comparison in terms of BRB fault feature between the mentioned machine and conventional 3-phase IM has also been performed. Assessment process has been performed initially using Finite Element Algorithm (FEA). Then, a prototype has been re-wound for semi-12-phase arrangement which is an alternative name of asymmetrical 6-phase IM. It has been confirmed experimentally that the BRB fault indicators, the Side Band Harmonic Components (SBHC) of stator current for asymmetrical 6-phase IM, are greater than those of their 3-phase counterparts. Analysis has been concluded with various BRB fault scenarios including single and double BRB cases with variable loading conditions. Furthermore, a fault classifier has also been proposed which could detect and classify the BRB fault for asymmetrical 6-phase IM.

Resolver is an electromagnetic sensor for calculating position and speed in motion control drive of electrical machines. Due to electromechanical structure of the machine, occurrence of the mechanical faults includes static and dynamic eccentricities (SE, DE) is inevitable. Even an inherent level of SE exists in newly manufactured machines. Furthermore, due to thin wires of the stator and rotor windings short circuit fault has also high probability. Since the accuracy of the resolver is significantly affected by electrical and mechanical faults, it is required to diagnose them at the very beginning of their development. In this paper, some indices are proposed for condition monitoring of wound rotor resolvers under mechanical and electrical fault. It is shown that the proposed index of each fault is unique and is not excited by other fault. The discussions are done using time stepping finite element method and the results are verified by experimental tests on prototype resolver.

Stator and rotor winding damages in rotating machines are result of electrical, mechanical, and thermal stress. Online magnetic field monitoring via permanently installed measuring coils inside air gap is a well-established methodology which enables winding fault detection. The paper deals with a new method for detection of stator and rotor winding inter-turn short circuits of synchronous machines and slip rings induction machines, as well as rupture of rotor bars and cage ring of induction machines. The method novelty is based on differential measurement of magnetic field by using two serial connected measuring coils. They are installed on the places (stator or rotor teeth) in the machine which have, by absolute value, equal magnetic vector potential. The distance between the measuring coils is n·tp, where tp is a pole pitch, and n = 1, 2, 3, 4,... is a multiple of the pole pitch. Measuring the coil-induced voltage enables us to detect stator and rotor winding faults, which means that measured voltage is approximately zero without fault and increases in the presence of fault. Analysis of the measuring signal allows us to detect and locate fault. With this new method it is possible with high sensitivity to determine winding fault, which enables more reliable fault detection. For example, in comparison with the motor current signature analysis method (the most widely used method for motor faults detection), this new method gives 200 times higher sensitivity to fault occurrence. Also, by using the DMFM method, faults can be detected in the time domain and there is no need for spectral or other complex signal analysis. This is very important because the measuring equipment used for machine fault detection can be simple and more economically acceptable. The DMFM method enables fault detection for even small machines with small expense in a very effective way. The only downside of the DMFM method is the fact that machine should be disassembled in order to install measuring coils. This problem is solved during the machine overhaul or during the manufacturing of the machine, when sensors can be easily implemented in the machine. For machines with large air gap, measuring coils can be installed without a machine disassembly. For the purpose of the method testing, numerous finite-element (FE) simulations on the 2- and 3-D machine models have been carried out to verify the method. Powerful numerical tools generate realistic results with properly selected starting and boundary conditions. By FEM models, actual machines with embedded measuring coils where created and simulated. The voltage induced inside the measuring coils is calculated for different machine states, load point and with and without a fault (broken rotor bar or inter-turn short circuit). Also, this method was experimentally validated via series of laboratory tests performed on the real electric machines specially designed for fault study (broken rotor bars, broken ring and inter-turn short circuits in a stator and rotor winding). Additionally, this method is applied on more than 20 real machines in industry. Due to the large amount of measured data, in this paper, it will be presented only one measurement performed on an induction motor on which we have detected one broken rotor bar. The thickness of the measuring coil designed in the printed circuit board technique is 0.3 mm. The number of turns is from 3 to 10. This new method and performed FEM calculations together with the experimental measurements improve fault detection portfolio knowledge that can be used in monitoring and diagnostics of rotating machines. Furthermore, this patent-pending method is already implemented in three innovative products placed on market (expert monitoring systems), so this method is fully confirmed in practice.

Comparative investigation of vibration and current monitoring for prediction of mechanical and electrical faults in induction motor based on multiclass-support vector machine algorithms

The on-line monitoring techniques which have normally been used to assess the condition of large HV squirrel-cage induction motors have been dedicated to detecting rotor dynamic problems and bearing wear [1]. This is usually achieved by sensing the shaft movement using eddy current displacement probes in the bearing housings. The technique has proved to be successful for detecting drive misalignment, a bent shaft, oil whirl, bearing eccentricity and wear [2]. Unfortunately, an HV induction motor can fail due to other fault mechanisms which are not simply mechanical or electrical but are often interrelated and complex [3]. For example, frequent direct-on-line (DOL) starting can cause broken rotor bars or end rings due to the large starting currents producing high mechanical and thermal stresses on the cage winding [4]. DOL starting also produces large end winding forces and core vibration during the initial transient. This can cause mechanical damage to the end winding insulation and electrical failure.

ResumenEn el presente trabajo se muestra y se aplica una metodología para el diagnóstico de fallas en máquinas eléctricas rotatorias utilizando la técnica de espectros de frecuencia de bandas laterales de las corrientes de fase.Se establecen sistemas de prueba de laboratorio para verificar las magnitudes de las bandas originadas por fallas tanto mecánicas como eléctricas.Se muestra una serie de resultados para un grupo motor de corriente directa-generador síncrono de 5 kVA desalineado y de dos motores de inducción de 5 HP tipo jaula de ardilla, uno con holgura en soporte de baleros y otro con barras rotas.Además, se comparan los resultados de los espectros empleando un sensor de corriente convencional contra una bobina de Rogowski, obteniendo resultados adecuados para detectar la presencia de falla en las máquinas.

Although the induction motor (IM) is known for its robustness and low cost of construction, it can happen that the it presents electrical, magnetic or mechanical faults. In this paper, a Hilbert-Huang Transform (HHT) spectral envelope of the stator current signature is proposed for the diagnosis of an IM rotor broken bar (RBB) fault. Firstly, the classical well-known spectral analysis Fast Fourier Transform (FFT) technique is applied to the stator current signal to obtain the harmonic frequency characterizing the RBB fault. Secondly, the HHT technique based on the ensemble empirical mode decomposition (EEMD) algorithm is proposed and applied to the stator current signal to obtain a function called the intrinsic mode function (IMF) containing the frequency that is related to the harmonic frequency characterizing the RBB fault. A comparative study is then carried out to illustrate the HHT technique merits and its superiority with respect to the FFT classical one. To test the effectiveness of the proposed HHT technique and validate the simulation results obtained, several experimental tests are also conducted using a test bench.

The stray flux in the vicinity of an electrical machine is inherent to its running. It is tied to the magnetic state of the machine and therefore can be affected by the presence of a fault in the machine. Accurate modeling of the external magnetic field is quite complicated and simple models are required in order to analyze the effect of an internal fault on the stray flux. This paper displays the possibilities offers by the stray flux for fault detection. A simple model of the external magnetic field is presented and a study in healthy and faulty conditions is developed. Two kind of fault in induction machine are considered: stator inter-turn short-circuit and rotor broken bar. Experimental results are in agreement with the theory, they highlight the differentiation between the radial flux and the axial flux.

This paper deals with the improvement of the switched reluctance motor's fault tolerant control for traction drives. The main purpose is to use a control technique which is able to retain the required speed for various load torques at minimal torque ripple also during one opened phase operation. The paper focuses on the comparative study of the current hysteresis control and direct instantaneous torque control (DITC) techniques.

Induction machines are widely used in industrial applications. Safety, reliability, efficiency, and performance are major concerns that direct the research activities in the field of electrical machines. Even though the induction machine is very reliable, many failures can occur such as bearing faults, air-gap eccentricity, and broken rotor bars. The challenge is, therefore, to detect them at an early stage in order to prevent breakdowns. In particular, stator current-based condition monitoring is an extensively investigated field for cost and maintenance savings. In this context, this paper deals with the assessment of a new stator current-based fault detection approach. Indeed, it is proposed to monitor induction machine bearings by means of stator current spectral subtraction, which is performed using short-time Fourier transform or discrete wavelet transform. In addition, diagnosis index based on the subtraction residue energy is proposed. The proposed bearing faults condition monitoring approach is assessed using simulations, issued from a coupled electromagnetic circuits approach-based simulation tool, and experiments on a 0.75-kW induction machine test bed.

Building electrical system fault diagnosis is the blank in the fault diagnosis field at home and abroad. The main reason is that the building electrical systems have many complex and huge subsystems; meanwhile, it is very hard to establish the mathematical model of system. By using the neural networks which is not depending on the model and using its advantage of convergence speed, the difficulties of building electrical system fault diagnosis can be well solved. This paper puts forward a method of fault diagnosis based on radial basis function neural network (hereinafter referred to as the RBF network) and applied it to building electrical system fault diagnosis. Beijing institute of civil engineering and architecture building intelligent experiment center provides building electrical test platform which can collect actual fault samples for RBF network training. After experiments and verification, RBF network’s accuracy and speed on fault diagnosis of building electrical system is significantly better than BP network. The effective RBF network in building electric system fault diagnosis field will have good engineering application prospect in the future.

Detection of half broken rotor bar fault in VFD driven induction motor drive using motor square current MUSIC analysis

Fault diagnosis and abnormal event management are key issues in process industry. For decades of research, many approaches have been proposed and put into practice. The Signed Digraph (SDG) model based diagnosis approach seems to be more effective in tracing the root causes of abnormality in large scale process system like petrochemical industry, especially when system becomes more complex nowadays. Bu t SDG model based diagnosis approach has two inherent deficiencies. Firstly, the SDG models developed by current method often hold false causalities which will cause spurious diagnosis results. Secondly, the diagnosis result does not indicate the mechanical or electrical root fault causes directly. The root mechanical or electrical fault causes are more helpful for maintenance than root abnormal state variables given by SDG results. According to these deficiencies of SDG, a systematic root fault causes tracing method is proposed. First of all, causal ordering analysis based on the many physicochemical equations and empirical formulas in process systems is introduced to construct accurate SDG models. Then, the hierarchical dependent relation between state variable of SDG and its root failure causes is described by polychromatic sets. The dependent relation is further transformed into Fault Trees (FTs) which are used to deduce the root faults causes. In this method, the SDG model and the FTs are integrated into one systematic diagnosis model. The top level of this model is used to represent state variables of different equipments in a process system, and the bottom level are used to deduce the root fault causes of the equipment abnormality. This method integrates the advantages of SDG and FTA. Two examples are illustrated to show the modeling and diagnosis process, and thereby verify the practicality and validity of the proposed approach in process system fault tracing.

Most often, broken rotor bar (BRB) frequency components are hardly detected in the stator current due to its low magnitude and its closeness to the supply frequency component. In this paper, a new approach is applied to overcome this drawback based on Pitch synchronous wavelet Transform (PSWT) under lower sampling rate. Compared to the traditional filtering techniques such as notch filter, PSWT can eliminate completely the fundamental frequency and doesn't cause any suppression at the frequency sideband. The use of a lower sampling rate is an important factor to optimize the cost and the computation time of the diagnosis systems. Statistical results show that the proposed method has a good performance to detect BRB under different load conditions and different fault severity.