Fault Indicators Research Articles

Interturn Short Fault Diagnosis Using Magnitude and Phase of Currents in Permanent Magnet Synchronous Machines.

With the increased demand for permanent magnet synchronous machines (PMSMs) in various industrial fields, interturn short fault (ITSF) diagnosis of PMSMs is under the limelight. In particular, to prevent accidents caused by PMSM malfunctions, it is difficult and greatly necessary to diagnose slight ITSF, which is a stage before the ITSF becomes severe. In this paper, we propose a novel fault indicator based on the magnitude and phase of the current. The proposed fault indicator was developed using analysis of positive-sequence current (PSC) and negative-sequence current (NSC), which means the degree of the asymmetry of the three-phase currents by ITSF. According to the analysis, as ITSF increases, the phase difference between PSC and NSC decreases and the magnitude of NSC increases. Therefore, the novel fault indicator is suggested as a product of the cosine value of the phase indicator and the magnitude indicator. The magnitude indicator is the magnitude of NSC, and the phase indicator means the phase difference between the PSC and the NSC. The suggested fault indicator diagnoses the degree of ITSF as well as slight ITSFs under various conditions by only measured three-phase currents. Experimental results demonstrate the effectiveness of our proposed method under various torque and speeds.

Analysis and Simulation of Inter-Turn Fault Of Synchronous Generator Using MATLAB

Abstract: The paper represents a comprehensive analysis of the inter-turn faults of a synchronous generator. In today's power system, we have used three phase synchronous generator to generate electric power, thus we all always try to reduce the loss in order to improve the efficiency of the alternator. We basically focus more on the stator winding faults. Stator winding has unbalanced loading, field failure and stator winding fault (including line to ground fault, line to line fault and double line to ground fault, three phase fault and inter turn fault) Alternator stator inter turn faults are considered to be rare and therefore they are not taken into that serious consideration while designing the protection system for the alternator. But we knew that there could be an inter-turn short circuit fault in the stator winding of the alternator. Early detection of inter turn faults will eliminate damage to the stator core and adjacent coils, reducing repair costs and generator outage times. Since inter turn fault causes imbalance in phase voltage, this concept is discussed in this paperfor inter turn fault detection. The negative sequence voltage of the generator is used as a fault indicator for inter turn fault detection. This new approach is done using MATLAB software. Also, this method works for external as well as internal fault and helps in keeping the generator winding healthy. Keywords: Inter Turn fault, internal and external faults, Internal Negative, Sequence Generator Voltage.

Main Engine Water Cooling Failure Monitoring and Detection on Ships using Interface Modbus Communication

Main engine failure will damage engine systems, reduce navigation safety, and cause serious marine accidents. The cooling system is very important to prevent the engine's mechanical efficiency from deteriorating and engine failure due to overheating. Therefore, a monitoring and error detection system is needed. The system is based on the Modbus RS485 communication and interface. The collection equipment uses components from the 7S-50MC diesel engine. PLC is used as processing equipment and Logic Panel Autonics S070 is used as an interface. Through these tests, error detection in this study can provide an indication of an error when an abnormal situation occurs. In addition, data monitoring and system fault indication can be displayed on the interface. Testing of the system proved that it complies with the Indonesian Shipping Bureau (BKI) regulations on engine warning systems. The research developed can form the basis of more complex and reliable monitoring and fault detection system for applications on ships.

ANALISIS KARAKTERISTIK MINYAK TRANSFORMATOR MENGGUNAKAN PENGUJIAN DISSOLVED GAS ANALYSIS (DGA) PADA IBT 1 GARDU INDUK

Electrical energy is an important factor to support people's lives. Electrical energy from the generator is channeled through a transmission line to the substation. Transformer (transformer) is a very important equipment in the electric power system. Inside there is oil which functions as an insulating medium and also as a cooling medium. This electrical and thermal load can cause dissolved gases in the transformer oil. This DGA (Dissolved Gas Analysis) test aims to determine the content of dissolved gases in transformer oil and then to analyze using three fault indication methods Total Dissolved Combustible Gas (TDCG) Method, Roger Ratio, and Duval Triangle. Results The analysis uses these three methods to determine corrective actions according to the condition of the transformer oil. Corrective action to return the dissolved gases in the transformer oil back to normal. Transformer oil filtering aims to remove or reduce potentially damaging gases from the transformer oil content. On December 29, 2020 the transformer experienced an increase in the content of flammable hydrogen gas and acetylene gas, the TDCG value was 1520 ppm and after corrective action the dissolved gas content decreased to 481 ppm, so that the condition of the transformer oil returned to normal.

Real-time identification of performance degradation stages of rolling element bearings by RVCFI

The rolling element bearing is a critical component, and its fault results in most of the faults in rotary machines. The performance condition monitoring of bearings can improve the safety of equipment and can provide a reasonable maintenance plan at the optimal time. To identify the initial fault time (IFT) and performance degradation stages of a bearing based on the sampling signal from the start to the current time in real time, the monitoring indicator of the initial fault (MIIF) and the monitoring indicator of the degradation stages (MIDS) are constructed. Firstly, multiple features with stable and high robustness are calculated based on the envelope spectrum of the vibration signal. And these features are standardized and integrated by the weighted-sum of multiple standardized features into a fused indicator. Then, the online variation coefficient of the fused indicator and its rate are calculated. Finally, the MIIF and MIDS can be obtained using the variation coefficient and cumulative sum based on the rate of the variation coefficient of the fused indicator, respectively. Meanwhile, the adaptivity and versatility of the multi-resolution singular value decomposition algorithm are also improved. The proposed methods are verified using two public tested data packets. It is shown from the results that the methods are able to efficiently identify the IFT and performance degradation stages of a bearing in a timely manner.

RLS-Based Algorithm for Detecting Partial Demagnetization under Both Stationary and Nonstationary Conditions

An algorithm was developed detect the partial demagnetization of permanent-magnet synchronous motors (PMSMs) under both stationary and nonstationary conditions. On the basis of the recursive least-squares (RLS) method, the vital component of fault-related harmonics in the current could be extracted on the line, and its proportion to fundamental component could be regarded as the indicator of partial demagnetization faults. The proposed algorithm is fairly easy to realize and could substitute conventional and complicated signal processing methods such as Fourier transform and wavelet transform when detecting partial demagnetization. Experiments with inverter-fed healthy and partially demagnetized PMSMs are carried out to substantiate the effectiveness of proposed algorithm under both stationary and nonstationary conditions. At the end, a way to eliminate the impact of eccentricity fault on the partial demagnetization diagnosis is given.

Bearing Fault Diagnosis under Time-Varying Speed and Load Conditions via Observer-Based Load Torque Analysis

Bearing fault is the most common failure in rotating machines, and bearing fault diagnosis (BFD) has been investigated using vibration, current, or acoustic signals. However, there are still challenges in some existing approaches. This study proposes a novel BFD method based on natural observer. Based on the analysis of the effects on the load torque signal caused by bearing faults in the permanent magnetic synchronous machine (PMSM), a modified natural observer was designed to reconstruct the load torque signal from electrical signals, acquiring a novel indicator without the additional sensor installed. Angular resampling was implemented to convert the non-stationary load torque signal into a stationary one to reduce the computational complexity. For full-auto diagnosis without human involvement, a threshold determination algorithm was also modified. Experimental validations were carried out under speed-varying and torque-varying conditions and were compared with phase current and q-axis current signals. The average signal-to-noise ratio (SNR) of the estimated load torque is about 8.65 times compared with the SNR of the traditional q-axis current. The effectiveness of the proposed method prior to the traditional PMSM bearing fault indicators is demonstrated by the order spectrum results.

Design and Implementation of Hybrid Transmission Line Protection Scheme Using Signal Processing Techniques

A hybrid scheme for transmission line protection (HSTLP) using the Stockwell transform (ST), Wigner distribution function (WDF), and alienation coefficient (ACF) is designed. Current signals are analyzed using the ST, WDF, and ACF to compute the Stockwell fault index (SFI), Wigner fault index (WFI), and alienation coefficient fault index (ACFI), respectively. These fault indexes are used to derive a hybrid signal processing fault index (HSPFI), which is implemented for the detection of transmission line fault events. The peak magnitude of HSPFI is compared with a preset threshold magnitude (TH) to identify the fault. The statistical formulation is proposed for fault location on the power transmission line. Fault classification is achieved using the number of faulty phases. A hybrid ground fault index (HGFI) is used to recognize the involvement of the ground during the fault event. This HGFI is determined by processing zero sequence current using ST and WDF. The performance of algorithm is tested by various case studies for fault impedance variation, variable sampling frequency, fault incidence angle variation, reverse power flow on transmission line, highly loaded line, different fault locations online, and noisy conditions. The algorithm is also validated to detect a fault on a practical transmission line of large area utility grid of Rajasthan Rajya Vidyut Prasaran Nigam Limited (RVPN) in India. The algorithm performs better than the Hilbert–Huang transform (HHT)-based protection scheme and wavelet transform (WT)-based protection scheme available in the literature in terms of mean error of fault location, fault location accuracy, and noise level. The proposed protection scheme efficiently detected, classified, and located the faulty events such as single-phase-to-ground fault (SPGF), two-phase fault (TPF), two-phase-to-ground fault (TPGF), three-line fault (TLF), and three-line-to-ground fault (TLGF). Transmission line fault location accuracy of 99.031% is achieved. The algorithm performs well even with a high noise level of 10 dB SNR.

Stator winding fault detection of an Alternator using MATLAB

The paper represents the comprehensive analysis of stator winding faults of an alternator. An alternator is main component of power system which plays a big role in whole system for security and stability. The conventional system only detect symmetrical and unsymmetrical faults, they will not detect inter turn fault. The fault detection of stator winding is an effective and important measure to ensure the safe operation of generator. Early detection of stator winding fault would eliminate damage to stator core & adjacent coils, also reduce repairing cost and outage time of generator. As stator winding fault causes unbalance in phase voltage, this concept is discussed in this paper for the detection of stator winding fault. The negative sequence voltage of generator is used as fault indicator for fault detection. This new method is done using MATLAB software. Also, this method operate for external as well as internal fault and helps the generator winding keep healthy. Keywords— stator winding fault, internal negative sequence voltage, internal and external faults.

Industrial process fault detection and diagnosis framework based on enhanced supervised kernel entropy component analysis

Most existing industrial process fault detection and diagnosis (FDD) techniques operate on data collected at a single scale and focus only on known faults. However, actual process data are inherently multiscale and unknown faults are always inevitable during system running. Therefore, they may perform unsatisfactorily. To tackle this problem, this paper develops a decentralized industrial process FDD framework using multiple enhanced supervised kernel entropy component analysis (enhanced SKECA) models, where each model acts as a fault indicator for one specific fault. Faults can be easily diagnosed by monitoring the outputs of all models within the framework. In particular, when new faults are identified, the framework can update itself only by adding the corresponding enhanced SKECA models without a complete rebuilding process. The monitoring results for the continuous stirred tank reactor (CSTR) process show that the proposed framework is effective in diagnosing both known and unknown faults.

Fault Diagnosis Method of Six-Phase Permanent Magnet Synchronous Motor Based on Vector Space Decoupling

Compared with the three-phase motor, the six-phase motor has lower torque ripple and higher fault tolerance performance, which makes it widely used in aviation, ships, industrial manufacturing, and other application fields. However, the probability of failure of the polyphase motor system increases with the increase in the number of phases. In order to deal with the open phase fault and power switch fault of the six-phase inverter, a fault diagnosis method for the six-phase inverter based on vector space decoupling (VSD) is proposed. The open phase fault index is first determined according to the VSD decoupling inverse transform and the current constraints. The fault index is then optimized from the perspective of preventing misdiagnosis and improving reliability, and the open phase fault can be diagnosed in one fundamental cycle. In addition, the current trajectory of harmonic plane after switch fault is analyzed, and the back propagation (BP) neural network is used to identify the harmonic plane current trajectory of different types of switch fault. Finally, the correctness and feasibility of the proposed fault diagnosis method are verified by simulations and experiments. The obtained results demonstrate that the proposed method can quickly and accurately locate the open phase fault and switch fault without additional hardware. The proposed method is simple, efficient, and robust.

A Multi-Turbine Approach for Improving Performance of Wind Turbine Power-Based Fault Detection Methods

The relationship between wind speed and the power produced by a wind turbine is expressed by its power curve. Power curves are commonly used to monitor the production performance of a wind turbine by asset managers to ensure optimal production. They can also be used as a tool to detect faults occurring on a wind turbine when the fault causes a decrease in performance. However, the wide dispersion of data generally observed around the reference power curve limits the detection performance of power curve-based techniques. Fault indicators, such as residuals, which measure the difference between the actual power produced and the expected power, are largely affected by this dispersion. To increase the detection performance of power-based fault detection methods, a hybrid solution of mono-multi-turbine residual generation is proposed in this paper to reduce the influence of the power curve dispersion. A new simulation framework, modeling the effect of wind nature (turbulent/laminar) on the wind turbine performance, is also proposed. This allows us to evaluate and compare the performances of two fault detection methods in their multi-turbine implementation. The results show that the application of a multi-turbine approach to a basic residual generation method significantly improves its detection performance and makes it as efficient as a more complex method.

Fault section location in urban distribution network based on fault marking

The goal is to propose an effective method for locating a fault segment in urban power distribution networks. Urban distribution networks have multiple outgoing lines, switches with multiple connections and have variable topology characteristics. It is found that the fault location method based on matrix algorithm has low adaptive capability and fault tolerance when dealing with complex and variable topology. Therefore, this paper proposes an efficient fault segment location method based on special fault indicators, which can significantly improve the accuracy and reliability of fault location. Accordingly, to improve the accuracy and reliability of fault location, a fault segment location method based on fault marking is proposed. The approach proposed in the paper relies on the analysis of the incident matrix, which describes the relationship between nodes and branches, and allows the use of graph theory. The branch state vector is added to obtain the adjacency matrix, which allows to describe the state of change in the dynamics of the distribution network topology. In the next step, a set of nodes and branches, which reflect the incoming and outgoing interconnections of the nodes, is established based on the selected direction of the network binding. According to the direction of the node fault current, the suspicious branches are identified and labeled to indicate the fault. By cumulative calculation and analysis of the labels, the target branches are screened out and the faulty sections of the city power supply network are identified. The results of the case study conducted in the paper show that the proposed method has good adaptability to the variable topology and increases the fault tolerance and accuracy of the developed matrix algorithm. The topological operating state of the network can be changed by controlling the switches to optimize the operation and improve the reliability of the power supply. Thus, the algorithm for fast and accurate fault location is of great importance for improving the safety and quality of urban power supply.

Delineation of structural lineaments of Southeast Nigeria using high resolution aeromagnetic data

Abstract Structural lineaments of Southeast Nigeria were delineated using high-resolution aeromagnetic data. Advanced methods such as enhanced horizontal gradient amplitude (EHGA) involving a 3D model and tilt depth method (TDM) were used in this study. The simulated magnetic model involving the EHGA detector positioned peaks over source borders and created sharp and clear edges for magnetic sources. The TDM is a cutting-edge depth determination method revealing the depth of the contacts and thickness of sedimentary bodies that vary from ∼500 to ∼2,500 m and ∼3,000 to ∼5,000 m, respectively. Lineaments of the study area were extracted from the EHGA map. The structural map showed the dominance of short-ranged (∼0.29 to ∼1.48 km) linear magnetic anomalies. In addition, regional geologic structures (∼2.8 to ∼24.5 km) occur at the western flank of the study area. In general, these structures trend in the NE–SW, NNE–SSW, N–S, E–W, and NW–SE directions. They are indicators of subsurface faults, contacts, and tectonic structures of the thermo-tectonic events associated with Santonian Abakaliki Anticlinorium, Tertiary-Recent Ikom-Mamfe Rift, and structural deformations of the basement rocks associated with the Oban Massif.

Zero-assignment and complex coefficient gain design of generalized proportional-integral observer for robust fault detection

Aiming at early detection of faults in dynamic systems subject to external periodic disturbances, this paper proposes a new generalized proportional-integral observer (GPIO) fault detection scheme with zero-pole joint optimization and novel complex coefficient gain (CCG) of residual evaluation. The focus of the proposed scheme is to reduce the adverse impacts caused by the semi-stationary periodic disturbance whose spectrum is uneven, with most energy being at some dominant frequencies. The proposed GPIO with a complex coefficient gain is designed in a two-stage procedure. In the first stage of zero assignment and pole optimization, the additional zeros introduced by the GPIO’s integration action are allocated to near the disturbance frequency. The gain of the transfer function matrix relating from the disturbances to the fault indicator signals is minimized by pole optimization. In the second stage of designing complex coefficient gain in residual evaluation, the unique feature of rank-deficient caused by the additional zeros assigned in stage one is further exploited to cancel the disturbances in the fault indicator signals (which is also referred to as the fault detection residual in this article). It is proved that, for an arbitrary periodic disturbance with a specific spectrum, the remnant components of the disturbance in the indicator signals generated by the GPIO can cancel each other by a complex gain vector, which can be determined by the zero eigenvalue’s left eigenvector of the rank-deficient of the disturbance transfer function matrix. The sufficient conditions for the convergence of the proposed fault detection filter are also given. Numerical examples illustrate the proposed method’s better performance in detecting minor faults.

Polyzwitterionic double-network ionogel electrolytes for supercapacitors with cryogenic-effective stability

Although hydrogel electrolytes have been widely used in the field of energy storage devices, additional conductive fillers with mismatched structural characteristics will inevitably reduce their both mechanical and electrochemical properties. How to achieve high ionic conductivity together with good mechanical strength is still a challenge. Herein, a high strength double-network polyzwitterionic electrolyte is designed by introducing water-soluble ionic liquids (EMIM-BF4) into a zwitterionic hydrogel through the superfast solvent exchange. The resulting polyzwitterionic electrolytes are superior to traditional polyzwitterionic hydrogels or polyvinyl alcohol (PVA) hydrogels with respect to their better tensile strength (1.9 MPa), higher ionic conductivity (7.24 mS/cm), and wider temperature adaptability (−60 to 50 °C). More importantly, the optimized symmetric assembled supercapacitor delivers high areal capacitance of 227.7 mF/cm2 at 0.5 mA/cm2, excellent rate capability, and high energy/power densities, greatly surpassing the performances of the system without polyzwitterions. It could be anticipated that the performance of this novel polyzwitterionic electrolyte-based supercapacitors will be further greatly improved with better structural electrodes. Moreover, we also implanted designed energy-storage systems to the subcutaneous tissue of the experimental rats and exhibited potential “on–off” variation of fault indicator LED. This work provides an effective method for the development of robust supercapacitors suitable for harsh environments, showing a great potential for real wearable and implanted applications in some extreme circumstances.

Multi-Source Data-Cooperated Neutral Low-Resistance Grounding Cable Grid Faulty Segment Identification

The multi-source data-driven faulted segment identification methodology has spurred research interest recently. Based on analyzing the post-fault physical features of the three-core cable earthed strap current, this study develops an approach to identify the faulty cable segment(s) of neutral low-resistance grounding distribution grids with distributed generators. To eliminate the potential uncertainty of recorded data, introducing Mixed Integer Linear Programming to construct the fault diagnosis model. The model employs traditional fault indications, including alarms from remote fault indicators and metering signals of zero-sequence current, to promote the validity of identifications. Under the causal logic constraint of events, employing the expected fault scenario with the minimum discrepancy to available data to determine the true faulty cable segment(s). Additionally, the model decision variables can be used as the reference for anomalous data determination. The effectiveness and robustness of the proposed methodology are validated by applying it to a simulation model based on a real-life three-core cable utility feeder.

Classification of Fault Severity in Induction Machine Systems Based on Temporal Convolutions and Recurrent Networks

Detection and severity identification of mechanical and electrical faults by means of noninvasive methods such as electrical signatures of induction machine have attracted much attention in recent years. Since operating conditions of machines and severity of faults in incipient stages influence the amplitude of fault index in the fault detection process, diagnosing fault occurrence and severity can be more complicated. In this study, an efficient method for fault detection and classification in induction machine based on deep neural networks is introduced. The introduced method applies the long short-term memory (LSTM) and fully convolutional neural networks (FCNs) in a conjoined manner. The authors use the FCN architecture for feature extraction from the time-series signal and augment it with LSTM to improve classification performance. This structure has not been previously applied for fault severity detection in induction machine systems. The authors avoid manual feature engineering and, by eliminating the preprocessing phase, directly use time series of electrical signals for fault detection and classifications. The experimental results have been carried out in different fault severities and loads. The analysis of the results and comparison with other deep and classical methods show that the faulty cases can be separated based on severity and load levels with a high accuracy (98.92%), which shows that the adopted architecture is successful in automatically extracting discriminative features from the signal.

A Decentralized Boltzmann-machine-based fault diagnosis method for sensors of Air Handling Units in HVACs

As a key module in a Heating, Ventilation, and Air Conditioning (HVAC) system, an Air Handling Unit (AHU) is controlled based on information collected by sensors to satisfy human thermal comfort and air quality requirements. Fault diagnosis is critical since it allows maintenance crews to know which faults have occurred to improve system availability. However, fault diagnosis in AHUs is challenging because of the following reasons. First, widely used fault indicators are correlated with changing environments, e.g., weather dynamics or occupants, thus may not be enough to distinguish faults. Second, existing decentralized fault diagnosis methods developed for sensors require solving many optimization problems, leading to high computational requirements. To overcome these challenges, this paper develops a decentralized Boltzmann-machine-based method. To address the first issue, residuals between actual values of several sensor readings and their estimates are considered as fault indicators since they are not related to changing environments. To address the second issue, a novel decentralized voting mechanism is developed based on the convergence characteristic of the Boltzmann machine to locate sensor faults while avoiding solving many optimization problems. However, the established Boltzmann machine usually has an asymmetric weight matrix, and thus it does not converge to the state estimates of sensors. To guarantee convergence, a new symmetrization method is developed to symmetrize the Boltzmann machine by adding an extra unit into the Boltzmann machine to reset the weight matrix while retaining the original voting. Experimental results demonstrate that our method can effectively diagnose sensor faults with high diagnostic accuracy.

Application of vibration analysis using time-frequency analysis to detect and predict mechanical failure during the nut manufacturing process

The nut manufacturing machine operates almost continuously throughout the day and, consequently, requires frequent maintenance, the duration of which depends on the type of fault. Unpredicted downtime can cause loss of production volume, inability to meet critical timelines, and product quality degradation. Vibration research was conducted on a six-die nut manufacturing machine, which experienced frequent faults on the fourth and sixth dies that exerted the greatest impact on the nut material. The application of several analytical techniques on vibration data – the fast Fourier Transform, Empirical Mode Decomposition, Multiscale Entropy, Shock Response Spectrum, and correlation – enabled the identification of fault indicators, which would be useful for predictive maintenance. These features were detectable up to 8 h 28 min and up to 1 h 9 min prior to failure or deterioration resulting from severe and regular damage, respectively. Faults included quality deterioration and die and non-die breakage, fusion, bending and cracking. This research can optimise machine efficiency through timely upkeep facilitated by predictive maintenance.