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The bearing faults may lead to expensive and catastrophic failures, which affect the reliability of power drivetrain in electromechanical system. To reduce impact of the bearing failures, accurate analysis and timely diagnosis are required for improved reliability of the electromechanical systems. In this work, an enhanced transient current signature analysis has been investigated for aircraft application using permanent magnet synchronous motor with bearing defects. The motor current under steady-state and transient conditions is acquired from an experimental test-rig with bearing defects at different loading and speed levels. The acquired signals are first investigated using frequency domain analysis and then compared with the time–frequency domain analysis such as wavelet analysis. The discrete wavelet transform is used to analyze on the calculated residual current for the bearing fault diagnosis. The proposed time–frequency-based technique is able to provide useful features that characterize the condition of the bearings on transient current. Further, back-propagation neural network is used over the calculated features which distinguishes the defective bearing from the healthy bearing with high accuracy.

In this paper, we propose a novel Health-Focused Optimal Power Flow (HF-OPF) to take into account the equipment health in operational and physical constraints. The health condition index is estimated based on the possible fault characteristics for generators and batteries. The paper addresses the need for understanding the relationship between health condition index and the operational constraints in OPF problems. Such a relationship is established through Finite Element Method (FEM)-based simulations. The simulations for generator and battery faults indicate the presence of an inflection point after which effect of fault becomes severe. A microgrid system is used to illustrate the performance of the proposed HF-OPF. The results show that health condition inflicts high cost of generation and can lead to infeasibility even with less critical faults., 5 Pages

The drive for more electric aircraft (MEA) architecture has resulted in the introduction of high power density electric machines for aircraft propulsion applications. The reliability of the electrical machines in such harsh environments is a challenge that is seldom addressed. The paper proposes, through simulation, the use of aging model to accurately estimate the lifetime of a machine with turn-to-turn short-circuit (TTSC) condition. The proposed aging model considers all the aging factors due to electrical, mechanical and thermal phenomena. The paper also puts forth a cumulative stress curve for various loading & environmental constraints and uses the stress curve with winding temperature to create an aging model to accurately predict and estimate the lifetime of the machine. A bottom-up approach through the developed model can be used to estimate the lifetime of the machine in real-time.

Electrical machines are essential parts of transportation systems. The reliability and availability of transportation systems can be significantly improved by the installation of diagnosis systems for these components. A critical challenge with the diagnosis process of electrical machines is to detect the incipient fault under the effects of inherent asymmetry and voltage imbalance. This paper addresses this problem, under the situation of stator winding interturn fault, by proposing a novel model-based approach. First, a model of asymmetric machines under fault is developed. The model analytically discriminates the effect of voltage imbalance, inherent asymmetry, and fault. Second, exploiting the developed model, a new diagnostic method, compensating the effects of both inherent asymmetry and voltage imbalance, thereby aiding in achieving incipient fault detection, is proposed. Third, as a result of the diagnostic method, a fault-dependent quantity, estimated using the measurements of only stator voltages and currents, is obtained. This quantity is a function of fault parameters, and hence, the method provides a solution to quantify the fault severity under the practical situation of unmeasurable fault parameters. In addition, a method has been envisaged for faulty phase identification. The theoretical development is supported by experimental results, where various fault severity levels and different load and voltage imbalance conditions are taken into account.

In this paper, a model-based integrated framework for diagnosis and prognosis of electrical machines is proposed. Since the fault progression, compared to the dynamics of the measured terminal quantities, can be considered as a slow process, it can be approximately modeled by the step changes of certain model-based parameters, representing fault severity levels. Diagnosis process can reliably detect the fault occurrence and severity changes, from the incipient stage of fault development, by which the parameter estimation and prognosis process, leading to calculation of remaining useful life, can be promptly commenced. A generic method using multiple fault indicators for diagnosis and particle filter for prognosis is proposed for electrical machines under stator winding interturn fault. The proposed framework is experimentally validated using real machine signatures consisting of only stator voltage and current measurements.

Any defect in the form of an axial crack or a circular defect in the propeller induces additional component in the centrifugal force and thereby producing an additional vibration characteristic in the propeller. The objective of this paper is to determine the translation of vibration to the permanent magnet synchronous machine (PMSM) which drives the propeller and in turn a triaxial accelerometer is used to detect the faults. A finite element based model is created consisting of the propeller, shaft, bearings and PMSM. The FEA modal analysis is performed to determine the natural frequency of the system and a harmonic response analysis is performed to determine the vibration harmonics at the natural frequency of the system. A comparison of the natural frequencies of the healthy system and the defective systems are obtained along with vibration characteristics for the different profiles and the load dependency of the vibration characteristics. It is observed that the x-axis and z-axis vibrations increase significantly for the defective propeller and it increases with load.

This paper presents an online technique to estimate the capacitance value of the LCL filter integrated with Active Front End Rectifier (AFER) used in marine vessels or offshore drilling/oil rigs. The proposed methodology is simple, robust and requires flux sensors in addition to the existing on-board sensors for grid-voltages, grid- and capacitor-currents measurements. The proposed online methodology is based on the fundamental components of inductor leakage fluxes, grid-voltages, grid- and capacitor-currents, which estimates both inductance and capacitance of the LCL filter. Based on the estimated values of the inductance and capacitance, a technique is proposed to estimate the resonant frequency of the LCL filter under real-time operating condition which can be used to predict the maintenance schedule before any catastrophic failure. The proposed technique has been validated in a laboratory based 11.3kW marine electric propulsion test-rig for 7.7%, 10% and 14% degradation in the capacitance value of the LCL filter under different loading conditions. The estimated capacitance values under real-time operating condition agree with the measured values under offline conditions for various degrees of capacitance degradation and loading conditions.

Wireless Sensor Network (WSN) is a physical bridge that connects the real world to the digital world. Due to recent advances in an Internet of things and Industrial 4.0, more extensive WSN devices are used in industries ranging from the aerospace system, smart grid, Unmanned Aerial Vehicle (UAV) and drone application to the military investigation. However, due to the nature of WSNs, attackers can easily deploy various security threats to make WSN network vulnerable and unstable. The easiest way to attack WSN network is through the jamming attack with a little bit knowledge of targeted communication system. Standardisation of wireless communications used in various industries makes the attackers easy to design a smart reactive jamming device. In this paper, a new method using a timing channel is proposed to defeat the reactive jammer. To validate the proposed method, a smart reactive jamming device, a transmitter and a receiver are implemented in hardware. Test results are presented, showing how the method can defeat the reactive jamming attack.

Inter-turn short circuit (ITSC) fault is one of the critical electrical faults in induction motors that affects the reliability of many industrial applications. Although the use of data-driven fault detection techniques have gained much interest, the main deterrent in using these approaches in detecting ITSC faults is in the generalization and robustness of the diagnosis. In this paper, a data-driven on-line fault detection framework, incorporated with multi-feature extraction/selection and multi-classifier ensemble is proposed, capable of detecting ITSC faults in induction motors (IMs) that subjected to variable operating conditions. By using the synchronous time series signals collected from the machines, multiple feature extraction/selection is explored to find the sensitive faulty features, and the different types of classification strategies is used to increase the diversity of single based models. With the increased diversity of the base learners, the fault detection accuracy is expected to be enhanced and the robustness can be guaranteed. The framework was implemented and tested using real data collected from a designed test bed, with the experimental results showing the effectiveness of the framework in detecting ITSC faults in IMs.

In this paper, a model-based approach is proposed to detect and diagnose stator winding fault in the Brushless wound-field synchronous generator (BWFSG). The extended Kalman filter is used as a state and parameter estimation technique for the proposed model-based approach. The mathematical model of the BWFSG with stator winding fault is developed and simplified for online implementation. An experimental test-rig is used to acquire the required inputs for the developed state estimation technique. The estimated rotor currents and fault parameter are analyzed to identify key signatures for condition monitoring (CM). The harmonic components such as the second harmonic components of the estimated field and damper currents, and the rms value of the estimated fault parameters are identified as suitable signatures for winding fault and diagnose. Based on the identified signatures, a model-based CM algorithm is proposed and validated in real time. The validation results confirmed that the proposed algorithm is able to detect and diagnose winding inter-turn short-circuit faults in real-time reliably.

Many industries have a growing awareness in utilizing new technologies to improve the reliability and availability of their systems. Prognostics, a subject concerned with the prediction of the remaining useful life (RUL), has been increasingly studied and applied to practical systems, such as electrical systems, over the past few years. Here, with the adoption of a data-driven prognostics framework, this paper proposed a health index (HI)-based prognostics method to predict the RUL of electrical machines. By assuming a linearly degrading HI over time, the proposed method predicts the RUL in two steps: 1) from input signals to HI; and then 2) mapping HI to RUL. The novelty of this method lies in the proposed dynamic HI smoothing approach where three characteristics of HI, namely monotonicity, gradualness, and consistency, are incorporated to smooth the current HI values with the previously predicted ones. Real data collected from eight electrical motors, subjected to accelerated thermal aging process, were used in the experimental studies, with the results showing the superiority of the proposed HI-based RUL prediction over the traditional direct RUL prediction (i.e., without HI).

Electrical and magnetic domain parameters for online condition monitoring of converter transformers used in marine power system is explored, where the traditional DGA and PD techniques are not much applicable. Current signal and leakage flux from transformer core and winding are studied, with an emulated inter-turn fault in a 10 kVA lab-scale converter transformer. It is expected that the presence of valve winding in converter transformer will pose a challenge, for condition monitoring as it has inherent harmonics. There is a need for an online monitoring technique which is simple, cost effective and rugged to diagnose the fault and raise an alarm on-board ship, as it may lack the right maintenance technician or a replacement transformer during the voyage. Method of emulating the inter-turn fault in transformer, experimental studies and the frequency domain results from electrical and magnetic sensors for condition monitoring are discussed.

For the purpose of monitoring the health conditions of electrical machines, a framework is proposed to establish the methods to provide an early warning to potential machine failures in data mining terminology. The framework consists of five stages including data segmentation, feature extraction/selection, multi-classifier ensemble, decision fusion and output, which is flexible and can be adapted for any known faults. The difference lies in the implementation choices of techniques and structures (e.g. number of classifiers) in the second to forth stage as well as the input requirements. As an example, the turn-to-turn short circuit fault of induction motor is used as the known fault in studies in this work. Simulation results show the effectiveness of the proposed techniques.

The role of transformer in electricity network reliability and safe operation is quite crucial. Therefore, their monitoring, maintenance and management is vitally important for utility operators. Simpler, non-invasive and online condition monitoring method which is sensitive to incipient faults is in demand for transformer diagnosis. Although vibration monitoring is considered as a valuable technique in industry for rotating machines, it is least explored for static electrical equipment such as transformer. In this study, core and winding vibrations in a dry type transformer is monitored using an accelerometer to diagnose the winding electrical and mechanical faults. Winding and core vibrations for various degrees of inter-turn fault, axial movement and disc displacement under different load conditions are studied.

In this paper, a hybrid modeling approach to model synchronous generator by combining the $dq0$ modeling with the winding function approach has been proposed. The proposed hybrid approach improves accuracy and reduces complexity of the synchronous generator model for winding fault such as turn-to-turn short-circuit (TTSC) . This paper also proposes a simple method to model localized saturation effect due to TTSC and presents an air-gap function for synchronous machine with winding fault to improve model accuracy under high degrees of TTSC fault. Experimental validation results show that the developed synchronous generator model with TTSC fault is able to provide improved accuracy and consistent performance under wider degrees of TTSC faults in the stator windings.

In condition monitoring of electrical machines, wireless sensors enables access to internal signals that are relatively unexplored compared to the conventional external measurements. This paper demonstrates the application of wireless sensor to measure the rotor field winding current in a Brushless Synchronous Generator and compare the fault detection capability with the stator exciter field current. The faults considered are the stator armature and rotor field winding inter-turn short circuit fault. From the experimental investigation, the rotor field current measurements obtained through a wireless sensor is found to be more effective in tracking the evolution of a fault.

The overall efficiency of a marine electrical power system is reliant on its condition monitoring algorithm. A competitive system has quick fault detection functionalities and maintenance enabled. This paper examines existing method to develop an online approach for superior detection of eccentricity fault in surface-mounted permanent magnet synchronous motors, in a marine propulsion setting. Detection of eccentricity fault is best carried out by monitoring vibration, air-gap flux density and induced emf. With the introduction of search coils, the proposed methodology aims to obtain the induced emf voltage in the air-gap. Search coil were found to decisively segregate fault symptoms appearing from other asymmetric interruptions such as load imbalance and input unbalanced voltage. Vibration in the motor was monitored by placing sensors on the stator yoke to detect vibration during various levels of eccentricity fault. In this research, eccentricity fault was simulated in 2D finite element analysis (FEA) platform using Ansys Maxwell and Ansys Mechanical. The simulated results shows a robust online based fault diagnosis method applicable for marine propulsion motors.

A multiple indicator based fault diagnosis and condition identification has been proposed in this work suitable for brushless synchronous generators (BLSG). One of the important challenges in BLSG interturn fault diagnosis is to identify whether the load imbalance exists alongwith the fault. The multiple indicator approach proposed in this work, is found to be useful, especially at incipient stages of fault development. The fault indicators are derived from the analytic fault model of the BLSG, and the technique is found to work well for complex conditions when fault and load imbalances occur together. The results show the applicability of the method to accommodate varying operating conditions of the machine as well as different imbalances in load.

A marine electrical power systems fate in terms of its quality and maintenance heavily relies on the efficiency of its fault detection. This paper analyses and develops an online approach to detecting winding and demagnetization faults in surface-mounted permanent magnet synchronous motor (SPMM) in a marine propulsion setting. The proposed methodology uses search coils to determine faults that reveals data to pinpoint which slot/magnet is affected by the faults, segregating fault symptoms from other asymmetric problems such as load imbalance and input unbalanced voltage. This paper is distinctive in its acute detection of faults even when the severity of the fault is very minimal. The proposed model used for simulation in two-dimensional (2-D) Finite Element Analysis (FEA) is a 3-phase, 42 poles, 54 slots fractional slot concentrated wound, SPMM. In addition to the use of search coils, the proposed research looks into the torque profile with various conditions of fault and analyzes the machine current using Hilbert-Huang transform. The simulated results reveal potential for the proposed method to provide reliable data on fault diagnosis of marine propulsion motors.

Fault analysis of marine generators is paramount to improve reliability and accessibility of a marine electrical power system. Inter-turns fault is presumably the most common fault in a PMSM and the symptoms associated with this fault in many machines are undetectable, depending on the level of fault. This paper proposes a novel method to use search coils to determine the inter-turns fault in a Surface Mounted Permanent Magnet Machine (SPMM). This technique would reveal data to pinpoint which slot is affected by the fault and how fault symptoms can easily be segregated from other asymmetric problems such as load imbalance and input unbalanced voltage. The proposed model used for simulation in FEA using MAXWELL 15 is a 3-phase, 42 poles, 54 slots fractional slot concentrated wound (double layer), surface mount PMSM. In addition to the use of search coils, the proposed research also looks into the torque profile with various conditions of fault, analyzes the machine current using symmetrical components and discrete wavelet transform.

A multiple model indicator based fault diagnosis and condition identification has been proposed and experimentally verified in this work. One of the important challenges in fault isolation is discriminating the fault with voltage imbalances, which always exists in the system. The multiple indicator approach proposed in this work, is found to be useful, especially at early stages of the fault. The fault indicators are derived from the analytic fault model of the induction machine, and the technique is found to work well for complex conditions when fault and imbalances occur together. The method is insular to load variations. The experimental results show the applicability of the method to accommodate varying operating conditions of the machine as well as the imbalances in supply.

In this paper, the SMART grid concepts and technology that can be potentially applied to address key challenges in future Naval vessels are analyzed. The IEEE standards for designing various modules for naval vessels such as Integrated Power System (IPS) are discussed and proposed potential architecture for power management module in naval vessel IPS to supply SMART grid concepts.

This paper presents a technique based on leakage flux to detect the winding fault in coupled inductors using a single flux sensor. An analytical model is developed and analysis on leakage flux for different load conditions and severity of fault is carried out. Using the developed model a comparative study of leakage flux at the outer limb and center of the yoke under the winding fault is carried out. The simulation study revealed that leakage flux at center of the yoke is found to be effective in identifying the fault. The analytical model is validated experimentally by using a three phase, star connected 1.2 H, 2.2 A coupled inductor. Both analytical model based and experimentally obtained results are found to follow the same trend in leakage flux during the winding short circuit conditions. The trend has been verified for different loading and severity conditions. The proposed technique is relatively simple to implement in real-time. The technique requires a single, inexpensive and low bandwidth hall-effect based flux sensor and is capable of detecting winding abnormality in a three phase coupled inductor.

Stator winding insulation failure has been one of the major faults in electrical machines due to the degradation of winding insulation materials. The turn-to-turn short circuit fault indicates an early stage of insulation failure. This fault produces distortion of space harmonics in the air gap magnetic field. Capturing these harmonics and reflecting their effect on the measurable quantities from stator would improve the accuracy of the model representing the machine under faulty condition. In this paper an approach for simulating a salient pole synchronous generator with stator inter-turn fault using a phase domain model is proposed. To account for the space harmonics in the model, Modified Winding Function Approach is utilized to compute the inductances of the machine winding. The developed model is compared with the experimental data for verifying the accuracy of the developed model.

Condition monitoring of the Brushless Wound Field Synchronous Generator (BWFSG) is important as it is widely used in mission and safety critical applications such as marine vessels. The frequency signatures in rotor field current are well known indicators to detect and diagnose stator winding short circuits in synchronous generators, in addition the damper bars current could also be used for fault detection and diagnosis. However, BWFSG these currents are not accessible. Hence, it is important to use the mathematical model and state estimation techniques to estimate these parameters. This paper compares the performance of state estimation techniques such as the Extended Kalman Filter (EKF) and Particle Filter (PF) in estimating field current and damper bars current for stator winding fault detection and diagnosis. The experimental validation results confirmed that the performance of the EKF is better than that of the PF in terms of number of stator winding fault signatures extracted from estimating the field current and damper bars currents. Thus, the future work proposed to use the EKF for developing Model-Based Condition Monitoring (MBCM) system for the BWFSG.

A multiple indicator based fault detection and isolation (FDI) is proposed in this paper for inter-turn fault in induction machines. The key step is the derivation of two indicators from the sequence component model of the induction machine. One main challenge in inter-turn fault isolation is discriminating with voltage imbalance, which invariably exists in the system. The identification of the inter-turn fault and information on voltage imbalance condition can be obtained by employing the proposed technique. Different types of voltage imbalances have been examined to show the robustness of the proposed approach. The method is also insular to load variations which may occur in the real conditions.

Transformer loading is quite crucial for industry as it has significant influence on transformer insulation deterioration as well as its remained life. Transformer load monitoring even becomes more substantial once transformer is operated in high ambient temperatures. Having real-time information about transformer thermal stress and insulation medium is indeed necessity. An intelligent system should be developed to monitor the transformer condition and giving online information on transformer insulation deterioration. Hence, this study is concentrated on an online prognosis system predicting transformer aging rate as well as remained life. Cast-resin transformer thermal modeling is discussed and influences of load and ambient temperature are investigated. Prognosis software is developed based on introduced model and transformer aging rate is examined through different simulation studies. At the end, developed prognosis system is utilized to keep the transformer aging rate on a specific value in course of transformer operating period. Different simulations on this plan are carried out and results are discussed.

This paper analyzes the feasibility of using the existing auxiliary winding in wound field brushless synchronous generator for condition monitoring. The auxiliary winding is the 4th winding placed in the stator of a three phase synchronous generator to feed exciter field through the automatic voltage regulator. This paper presents the model of a wound field synchronous generator with stator winding fault and auxiliary winding to perform simulation study for identifying the fault signatures in auxiliary winding voltage. The identified fault signatures are validated with the experimental setup. The analysis shows that the auxiliary winding voltage harmonics can be used to detect and diagnose stator winding turn-to-turn short circuit fault. Hence, it is feasible to use auxiliary winding voltage for condition monitoring of wound field brushless synchronous generators.

Stator winding inter-turn fault is a common fault in induction machines. A substantial number of works have been developed for detection of this fault; however, there are only a few works on fault parameter estimation and faulty phase identification. In particular, there have been no works dealing with estimation of fault loop resistance. This paper presents a method for estimating the fraction of shorted turns and the fault loop resistance and identifying the faulty phase under steady-state condition. It provides a closed-form solution and hence is applicable to online fault detection and identification. Simulation results demonstrate the effectiveness of the proposed method.