Oil health index calculation and incipient fault diagnosis in power transformers using fuzzy logic

In this paper, a new fuzzy logic (FL) model is proposed for assessing the health status of power transformers. In addition, the detection of incipient faults is achieved where two or more faults exist simultaneously. The process is carried out by integrating a fuzzy logic model with the conventional International Electric Committee (IEC) ratio codes method. As transformer oil insulation deteriorates, excess percentages of dissolved gases such as hydrogen, methane, ethane, acetylene and ethylene are induced within the trasnformer. The status of oil health is generally assessed using these gas concentrations. Therefore, in the proposed model, 31 fuzzy rules are designed based on the severity levels of these gases in order to determine the health index (HI) of the oil. Similarly, any incipient faults along with their severity are also detected using the proposed fuzzy logic model with 22 expert rules. To validate the proposed fuzzy logic model, the data for dissolved gases in 50 working transformers operated by the Himachal Pradesh State Electricity Board (HPSEB), India, are collected. Over the years, calculations for the health index have been performed using conventional dissolved gas analysis (DGA) interpretation methods. The shortcomings of these methods, such as non-reliability and inaccuracy, are successfully overcome using the proposed model. The detection of incipient faults is normally performed using key gas, Rogers ratios, the Duval triangle, Dornenburg ratios, modified Rogers ratios and the IEC ratio codes methods. The shortcomings of these conventional ratio code methods in identifying incipient faults in some typical cases, ie multiple incipient fault cases, are overcome by the proposed fuzzy logic model.

Intelligent identification of incipient rolling bearing faults based on VMD and PCA-SVM

Rolling bearings are the key components of rotating machinery. Incipient fault diagnosis of bearing plays an increasingly important role in guaranteeing normal and safe operation of rotating machinery. However, because of the high complexity of the fault feature extraction, the incipient faults of rolling bearings are difficult to diagnose. To solve this problem, this paper presents a new incipient fault intelligent identification method of rolling bearings based on variational mode decomposition (VMD), principal component analysis (PCA), and support vector machines (SVM). In the proposed method, the bearing vibration signals are decomposed by using VMD, and a series of intrinsic mode functions (IMFs) with different frequencies are obtained. Then, the energy and kurtosis values of each IMF are calculated to reveal the intrinsic characteristics of the vibration signals in different scales. Finally, all energy and kurtosis values of IMFs are processed via PCA and subsequently fed into SVM to achieve the bearing fault identification automatically. The effectiveness of this method is verified through the experimental bearing data. The verification results indicate that the proposed method can effectively extract the bearing fault features and accurately identify the bearing incipient faults, and outperform the two compared methods obviously in identification accuracy and computation time.

Squirrel cage induction motor fault diagnosis using lissajous curve of an auxiliary winding voltage: case of broken bars

The detection of incipient faults has attracted industrials and researchers specific attention in order to prevent the motor breakdown, improve its reability and increase its lifetime. This paper presents a squirrel cage induction machine broken bar and rings diagnosis approach. This technic uses a new monitored signal as an auxiliary winding voltage related to a small coil inserted between two stator phases. Monitoring behaviors of the Lissajous curve of this auxiliary winding voltage park components under different load levels is the main key of this study. For this purpose, the squirrel cage induction machine modeling and the explicit expressions developed for the inserted winding voltage and its Park components will be presented. Then, an induction machine with different broken cases: one broken bar, two broken bars, broken end ring and broken bars with end ring are investigated. The simulation results confirm the validity of the proposed approach.

Broken Rotor Bar Detection in Induction Motors through Contrast Estimation

Induction motors (IM) are key components of any industrial process; hence, it is important to carry out continuous monitoring to detect incipient faults in them in order to avoid interruptions on production lines. Broken rotor bars (BRBs), which are among the most regular and most complex to detect faults, have attracted the attention of many researchers, who are searching for reliable methods to recognize this condition with high certainty. Most proposed techniques in the literature are applied during the IM startup transient, making it necessary to develop more efficient fault detection techniques able to carry out fault identification during the IM steady state. In this work, a novel methodology based on motor current signal analysis and contrast estimation is introduced for BRB detection. It is worth noting that contrast has mainly been used in image processing for analyzing texture, and, to the best of the authors’ knowledge, it has never been used for diagnosing the operative condition of an induction motor. Experimental results from applying the approach put forward validate Unser and Tamura contrast definitions as useful indicators for identifying and classifying an IM operational condition as healthy, one broken bar (1BB), or two broken bars (2BB), with high certainty during its steady state.

A Method for Detecting Incipient Faults in Satellites Based on Dynamic Linear Discriminant Analysis

Timely detection and treatment of possible incipient faults in satellites will effectively reduce the damage and harm they could cause. Although much work has been done concerning fault detection problems, the related questions about satellite incipient faults are little addressed. In this paper, a new satellite incipient fault detection method was proposed by combining the ideas of deviation in unsupervised fault detection methods and classification in supervised fault detection methods. First, the proposed method uses dynamic linear discriminant analysis (LDA) to find an optimal projection vector that separates the in-orbit data from the normal historical data as much as possible. Second, under the assumption that the parameters obey a multidimensional Gaussian distribution, it applies the normal historical data and the optimal projection vector to build a normal model. Finally, it employs the noncentral F-distribution to test whether a fault has occurred. The proposed method was validated using a numerical simulation case and a real satellite fault case. The results show that the method proposed in this paper is more effective at detecting incipient faults than traditional methods.

A Satellite Incipient Fault Detection Method Based on Decomposed Kullback–Leibler Divergence

Detection of faults at the incipient stage is critical to improving the availability and continuity of satellite services. The application of a local optimum projection vector and the Kullback–Leibler (KL) divergence can improve the detection rate of incipient faults. However, this suffers from the problem of high time complexity. We propose decomposing the KL divergence in the original optimization model and applying the property of the generalized Rayleigh quotient to reduce time complexity. Additionally, we establish two distribution models for subfunctions F1(w) and F3(w) to detect the slight anomalous behavior of the mean and covariance. The effectiveness of the proposed method was verified through a numerical simulation case and a real satellite fault case. The results demonstrate the advantages of low computational complexity and high sensitivity to incipient faults.

Influence of Regional Erosion and Sedimentary Loading on Fault Activities in Active Fold-Thrust Belts: Insights From Discrete Element Simulation and the Southern and Central Longmen Shan Fold-Thrust Belt

Four groups of discrete element models (DEMs) were set-up to simulate and analyze the influence of regional erosion and sedimentary loading on the formation and spatial-temporal evolution of faults in the southern and central Longmen Shan (LMS) active fold-thrust belt. The interior characteristics of faults in the southern and central LMS fold-thrust belt were also evaluated during the interaction of tectonic processes and surface processes according to the stress-strain analysis from DEM results. The results showed that synkinematic erosion promoted the reactivation of pre-existing faults in thrust wedges and also retarded the formation and development of new incipient faults in the pre-wedge regions. Meanwhile, synkinematic sedimentation also delayed the development of new incipient faults in the pre-wedge regions by promoting the development of thrust faults in the front of thrust wedges, causing these thrust wedges in supercritical stages with relatively narrow wedge lengths. According to these DEM results, we infer that: 1) The characteristics of erosion and sedimentation in the central and southern LMS have important influences on the activities of large faults which are extended into the deep detachment layer; 2) Besides differential erosion, the differential sedimentary loading may also be one of the important factors for the along-strike differential evolution of the LMS fold-thrust belt. This kind of differential deposition may lead to differential fault activity and uplift in the interior thrust wedge and pre-wedge region in the central and southern LMS; 3) Compared to the northern LMS, the central LMS and southern LMS is more conducive to the occurrence of earthquakes, because of synkinematic sedimentation (such as the growth of Chengdu plain) has a greater blocking effect on the stress propagation and strain convergence on the fault planes of front faults of an active thrust wedge.