Improved reliability in diagnosing faults using multivariate statistics

This paper analyses multivariate statistical techniques for identifying and isolating abnormal process behaviour. These techniques include contribution charts and variable reconstructions that relate to the application of principal component analysis (PCA). The analysis reveals firstly that contribution charts produce variable contributions which are linearly dependent and may lead to an incorrect diagnosis, if the number of principal components retained is close to the number of recorded process variables. The analysis secondly yields that variable reconstruction affects the geometry of the PCA decomposition. The paper further introduces an improved variable reconstruction method for identifying multiple sensor and process faults and for isolating their influence upon the recorded process variables. It is shown that this can accommodate the effect of reconstruction, i.e. changes in the covariance matrix of the sensor readings and correctly re-defining the PCA-based monitoring statistics and their confidence limits. (c) 2006 Elsevier Ltd. All rights reserved.

Decoupled-DFIG fault ride-through strategy for enhanced stability performance during grid faults

This paper proposes a decoupled fault ride-through strategy for a doubly fed induction generator (DFIG) to enhance network stability during grid disturbances. The decoupled operation proposes that a DFIG operates as an induction generator (IG) with the converter unit acting as a reactive power source during a fault condition. The transition power characteristics of the DFIG have been analyzed to derive the capability of the proposed strategy under various system conditions. The optimal crowbar resistance is obtained to exploit the maximum power capability from the DFIG during decoupled operation. The methods have been established to ensure proper coordination between the IG mode and reactive power compensation from the grid-side converter during decoupled operation. The viability and benefits of the proposed strategy are demonstrated using different test network structures and different wind penetration levels. Control performance has been benchmarked against existing grid code standards and commercial wind generator systems, based on the optimal network support required (i.e., voltage or frequency) by the system operator from a wind farm installed at a particular location.

Optimization of FRT Active Power Performance of a DFIG during Transient Grid Faults

Optimal fault ride-through (FRT) conditions for a doubly-fed induction generator (DFIG) during a transient grid fault are analyzed with special emphasis on improving the active power generation profile. The transition states due to crowbar activation during transient faults are investigated to exploit the maximum power during the fault and post-fault period. It has been identified that operating slip, severity of fault and crowbar resistance have a direct impact on the power capability of a DFIG, and crowbar resistance can be chosen to optimize the power capability. It has been further shown that an extended crowbar period can deliver enhanced inertial response following the transient fault. The converter protection and drive train dynamics have also been analyzed while choosing the optimum crowbar resistance and delivering enhanced inertial support for an extended crowbar period.

Analysis of VSC-based HVDC System under DC Faults

This paper analyzes the behavior of a Voltage Source Converter Based HVDC system under DC cable fault conditions. The behavior of the HVDC system during a permanent line-to-earth fault is analyzed, outlining the systems configuration and behavior at each stage of the fault timeline. Operation of the proposed system under a single earthing configurations i.e. Converter (solid) earthed/AC transformer unearthed, was analyzed and simulated, with particular attention paid to the converters operation. It was observed that the development of potential earth loops within the system as a result of DC line- toearth faults leads to substantial overcurrent and results in system configuration oscillation.

A predictive maintenance system for integral type faults based on support vector machines:An application to ion implantation

In semiconductor fabrication processes, effective management of maintenance operations is fundamental to decrease costs associated with failures and downtime. Predictive Maintenance (PdM) approaches, based on statistical methods and historical data, are becoming popular for their predictive capabilities and low (potentially zero) added costs. We present here a PdM module based on Support Vector Machines for prediction of integral type faults, that is, the kind of failures that happen due to machine usage and stress of equipment parts. The proposed module may also be employed as a health factor indicator. The module has been applied to a frequent maintenance problem in semiconductor manufacturing industry, namely the breaking of the filament in the ion-source of ion-implantation tools. The PdM has been tested on a real production dataset. © 2013 IEEE.

Identifying PV module mismatch faults by a thermography-based temperature distribution analysis

Photovoltaic (PV) solar power generation is proven to be effective and sustainable but is currently hampered by relatively high costs and low conversion efficiency. This paper addresses both issues by presenting a low-cost and efficient temperature distribution analysis for identifying PV module mismatch faults by thermography. Mismatch faults reduce the power output and cause potential damage to PV cells. This paper first defines three fault categories in terms of fault levels, which lead to different terminal characteristics of the PV modules. The investigation of three faults is also conducted analytically and experimentally, and maintenance suggestions are also provided for different fault types. The proposed methodology is developed to combine the electrical and thermal characteristics of PV cells subjected to different fault mechanisms through simulation and experimental tests. Furthermore, the fault diagnosis method can be incorporated into the maximum power point tracking schemes to shift the operating point of the PV string. The developed technology has improved over the existing ones in locating the faulty cell by a thermal camera, providing a remedial measure, and maximizing the power output under faulty conditions.

Mitigating the Impact of Faults in Unreliable Memories for Error-Resilient Applications

Inherently error-resilient applications in areas such as signal processing, machine learning and data analytics provide opportunities for relaxing reliability requirements, and thereby reducing the overhead incurred by conventional error correction schemes. In this paper, we exploit the tolerable imprecision of such applications by designing an energy-efficient fault-mitigation scheme for unreliable data memories to meet target yield. The proposed approach uses a bit-shuffling mechanism to isolate faults into bit locations with lower significance. This skews the bit-error distribution towards the low order bits, substantially limiting the output error magnitude. By controlling the granularity of the shuffling, the proposed technique enables trading-off quality for power, area, and timing overhead. Compared to error-correction codes, this can reduce the overhead by as much as 83% in read power, 77% in read access time, and 89% in area, when applied to various data mining applications in 28nm process technology.

Fault Diagnosis in Internal Combustion Engines Using Nonlinear Multivariate Statistics

This paper presents a statistical-based fault diagnosis scheme for application to internal combustion engines. The scheme relies on an identified model that describes the relationships between a set of recorded engine variables using principal component analysis (PCA). Since combustion cycles are complex in nature and produce nonlinear relationships between the recorded engine variables, the paper proposes the use of nonlinear PCA (NLPCA). The paper further justifies the use of NLPCA by comparing the model accuracy of the NLPCA model with that of a linear PCA model. A new nonlinear variable reconstruction algorithm and bivariate scatter plots are proposed for fault isolation, following the application of NLPCA. The proposed technique allows the diagnosis of different fault types under steady-state operating conditions. More precisely, nonlinear variable reconstruction can remove the fault signature from the recorded engine data, which allows the identification and isolation of the root cause of abnormal engine behaviour. The paper shows that this can lead to (i) an enhanced identification of potential root causes of abnormal events and (ii) the masking of faulty sensor readings. The effectiveness of the enhanced NLPCA based monitoring scheme is illustrated by its application to a sensor fault and a process fault. The sensor fault relates to a drift in the fuel flow reading, whilst the process fault relates to a partial blockage of the intercooler. These faults are introduced to a Volkswagen TDI 1.9 Litre diesel engine mounted on an experimental engine test bench facility.

Environmental Isotopes as Indicators for Ground Water Recharge to Fractured Granite

To assess the contribution of accumulated winter precipitation and glacial meltwater to the recharge of deep ground water flow systems in fracture crystalline rocks, measurements of environmental isotope ratios, hydrochemical composition, and in situ parameters of ground water were performed in a deep tunnel. The measurements demonstrate the significance of these ground water recharge components for deep ground water flow systems in fractured granites of a high alpine catchment in the Central Alps, Switzerland. Hydrochemical and in situ parameters, as well as d18O in ground water samples collected in the tunnel, show only small temporal variations. The precipitation record of d18O shows seasonal variations of ~14‰ and a decrease of 0.23‰ ± 0.03‰ per 100 m elevation gain. d2H and d18O in precipitation are well correlated and plot close to the meteoric water line, as well as d2H and d18O in ground water samples, reflecting the meteoric origin of the latter. The depletion of 18O in ground water compared to 18O content in precipitation during the ground water recharge period indicates significant contributions from accumulated depleted winter precipitation to ground water recharge. The hydrochemical composition of the encountered ground water, Na-Ca-HCO3-SO4(-F), reflects an evolution of the ground water along the flowpath through the granite body. Observed tritium concentrations in ground water range from 2.6 to 16.6 TU, with the lowest values associated with a local negative temperature anomaly and anomalous depleted 18O in ground water. This demonstrates the effect of local ground water recharge from meltwater of submodern glacial ice. Such localized recharge from glaciated areas occurs along preferential flowpaths within the granite body that are mainly controlled by observed hydraulic active shear fractures and cataclastic faults.