Continuous remote emissions monitoring – the lynchpin for air quality management

Diesel particulate matter (DPM), in particular, has been likened in a somewhat inflammatory manner to be the ‘next asbestos’. From the business change perspective, there are three areas holding the industry back from fully engaging with the issue: 1. There is no real feedback loop in any operational sense to assess the impact of investment or application of controls to manage diesel emissions. 2. DPM are getting ever smaller and more numerous, but there is no practical way of measuring them to regulate them in the field. Mass, the current basis of regulation, is becoming less and less relevant. 3. Diesel emissions management is generally wholly viewed as a cost, yet there are significant areas of benefit available from good management. This paper discusses a feedback approach to address these three areas to move the industry forward. The six main areas of benefit from providing a feedback loop by continuously monitoring diesel emissions have been identified: 1. Condition-based maintenance. Emissions change instantaneously if engine condition changes. 2. Operator performance. An operator can use a lot more fuel for little incremental work output through poor technique or discipline. 3. Vehicle utilisation. Operating hours achieved and ratios of idling to under power affect the proportion of emissions produced with no economic value. 4. Fuel efficiency. This allows visibility into other contributing configuration and environmental factors for the vehicle. 5. Emission rates. This allows scope to directly address the required ratio of ventilation to diesel emissions. 6. Total carbon emissions - for NGER-type reporting requirements, calculating the emissions individually from each vehicle rather than just reporting on fuel delivered to a site.

Fault symptoms of rolling element bearings under variable operating conditions - a multi domain analysis

Diagnostics of rolling element bearings have been traditionally developed for constant operating conditions, and sophisticated techniques, like Spectral Kurtosis or Envelope Analysis, have proven their effectiveness by means of experimental tests, mainly conducted in small-scale laboratory test-rigs. Algorithms have been developed for the digital signal processing of data collected at constant speed and bearing load, with a few exceptions, allowing only small fluctuations of these quantities. Owing to the spreading of condition based maintenance in many industrial fields, in the last years a need for more flexible algorithms emerged, asking for compatibility with highly variable operating conditions, such as acceleration/deceleration transients. This paper analyzes the problems related with significant speed and load variability, discussing in detail the effect that they have on bearing damage symptoms, and propose solutions to adapt existing algorithms to cope with this new challenge. In particular, the paper will i) discuss the implication of variable speed on the applicability of diagnostic techniques, ii) address quantitatively the effects of load on the characteristic frequencies of damaged bearings and iii) finally present a new approach for bearing diagnostics in variable conditions, based on envelope analysis. The research is based on experimental data obtained by using artificially damaged bearings installed on a full scale test-rig, equipped with actual train traction system and reproducing the operation on a real track, including all the environmental noise, owing to track irregularity and electrical disturbances of such a harsh application.

Effectiveness of MED for Fault Diagnosis in Roller Bearings

Diagnostics of rolling element bearings is usually performed by means of vibration signals measured by accelerometers placed in the proximity of the bearing under investigation. The aim is to monitor the integrity of the bearing components, in order to avoid catastrophic failures, or to implement condition based maintenance strategies. In particular, the trend in this field is to combine in a single algorithm different signal-enhancement and signal-analysis techniques. Among the first ones, Minimum Entropy Deconvolution (MED) has been pointed out as a key tool able to highlight the effect of a possible damage in one of the bearing components within the vibration signal. This paper presents the application of this technique to signals collected on a simple test-rig, able to test damaged industrial roller bearings in different working conditions. The effectiveness of the technique has been tested, comparing the results of one undamaged bearing with three bearings artificially damaged in different locations, namely on the inner race, outer race and rollers. Since MED performances are dependent on the filter length, the most suitable value of this parameter is defined on the basis of both the application and measured signals. This represents an original contribution of the paper.

Diagnostics of rolling element bearings for the traction system of high speed trains : experimental evidences

Monitoring of the integrity of rolling element bearings in the traction system of high speed trains is a fundamental operation in order to avoid catastrophic failures and to implement effective condition-based maintenance strategies. Diagnostics of rolling element bearings is usually based on vibration signal analysis by means of suitable signal processing techniques. The experimental validation of such techniques has been traditionally performed by means of laboratory tests on artificially damaged bearings, while their actual effectiveness in industrial applications, particularly in the field of rail transport, remains scarcely investigated. This paper will address the diagnostics of bearings taken from the service after a long term operation on a high speed train. These worn bearings have been installed on a test-rig, consisting of a complete full-scale traction system of a high speed train, able to reproduce the effects of wheel-track interaction and bogie-wheelset dynamics. The results of the experimental campaign show that suitable signal processing techniques are able to diagnose bearing failures even in this harsh and noisy application. Moreover, the most suitable location of the sensors on the traction system is also proposed.

Experimental evidences in bearing diagnostics for traction system of high speed trains

Rolling element bearings are the most critical components in the traction system of high speed trains. Monitoring their integrity is a fundamental operation in order to avoid catastrophic failures and to implement effective condition based maintenance strategies. Generally, diagnostics of rolling element bearings is usually performed by analyzing vibration signals measured by accelerometers placed in the proximity of the bearing under investigation. Several papers have been published on this subject in the last two decades, mainly devoted to the development and assessment of signal processing techniques for diagnostics. The experimental validation of such techniques has been traditionally performed by means of laboratory tests on artificially damaged bearings, while their actual effectiveness in specific industrial applications, particularly in rail industry, remains scarcely investigated. This paper is aimed at filling this knowledge gap, by addressing the diagnostics of bearings taken from the service after a long term operation on the traction system of a high speed train. Moreover, in order to test the effectiveness of the diagnostic procedures in the environmental conditions peculiar to the rail application, a specific test-rig has been built, consisting of a complete full-scale train traction system, able to reproduce the effects of wheeltrack interaction and bogie-wheelset dynamics. The results of the experimental campaign show that suitable signal processing techniques are able to diagnose bearing failures even in this harsh and noisy application. Moreover, the most suitable location of the sensors on the traction system is proposed, in order to limit their number.

Enhancements to dielectric response measurements of oil-paper insulation for online and offline applications

The research introduces a promising technique for monitoring the degradation status of oil-paper insulation systems of large power transformers in an online mode and innovative enhancements are also made on the existing offline measurements, which afford more direct understanding of the insulation degradation process. Further, these techniques benefit from a quick measurement owing to the chirp waveform signal application. The techniques are improved and developed on the basis of measuring the impedance response of insulation systems. The feasibility and validity of the techniques was supported by the extensive simulation works as well as experimental investigations.

Data driven modeling for power transformer lifespan evaluation

Large sized power transformers are important parts of the power supply chain. These very critical networks of engineering assets are an essential base of a nation’s energy resource infrastructure. This research identifies the key factors influencing transformer normal operating conditions and predicts the asset management lifespan. Engineering asset research has developed few lifespan forecasting methods combining real-time monitoring solutions for transformer maintenance and replacement. Utilizing the rich data source from a remote terminal unit (RTU) system for sensor-data driven analysis, this research develops an innovative real-time lifespan forecasting approach applying logistic regression based on the Weibull distribution. The methodology and the implementation prototype are verified using a data series from 161 kV transformers to evaluate the efficiency and accuracy for energy sector applications. The asset stakeholders and suppliers significantly benefit from the real-time power transformer lifespan evaluation for maintenance and replacement decision support.

Statistical gear health analysis which is robust to fluctuating loads and operating speeds

Condition-based maintenance is concerned with the collection and interpretation of data to support maintenance decisions. The non-intrusive nature of vibration data enables the monitoring of enclosed systems such as gearboxes. It remains a significant challenge to analyze vibration data that are generated under fluctuating operating conditions. This is especially true for situations where relatively little prior knowledge regarding the specific gearbox is available. It is therefore investigated how an adaptive time series model, which is based on Bayesian model selection, may be used to remove the non-fault related components in the structural response of a gear assembly to obtain a residual signal which is robust to fluctuating operating conditions. A statistical framework is subsequently proposed which may be used to interpret the structure of the residual signal in order to facilitate an intuitive understanding of the condition of the gear system. The proposed methodology is investigated on both simulated and experimental data from a single stage gearbox. © 2011 Elsevier Ltd. All rights reserved.

基于时间序列数据挖掘的旋转机械预诊断方法研究

基于状态的维护(CBM, Condition Based Maintenance)是近年来新兴的一种设 备维护策略，它的基本理念是在机械设备需要维护的时候才对其进行维护，强调 维护要及时、准确和经济。采用这种维护策略，能够提高工业生产的安全性和可 靠性，系统地降低企业运营成本。 机械设备状态预诊断是实现CBM 的核心支撑技术，对其进行深入研究，对推 动CBM 的发展具有重要意义。但是，由于相关研究起步不久，目前预诊断技术还 未能得到很好的实现，研究人员有必要不断地尝试各种新的有效方法来更好地解 决这一问题，加快其实现方法与技术应用的成熟进程。基于此，本文从数据挖掘 的角度，探索了机械设备预诊断新的解决方法和途径，深入研究和探讨了基于时 间序列数据挖掘的旋转机械预诊断方法。本文的主要工作包括： 1. 结合CBM 的基本理念和应用实际的需求，对机械设备状态预诊断的基本 内涵进行了系统分析。将状态评估、故障预测和剩余有效使用寿命预测三个预诊 断基本功能进一步抽象，提出了包含特征提取、状态预测和模式匹配三个子问题 的预诊断一般流程模式。在详细分析机械设备状态预诊断理论方法和应用技术研 究现状的基础上，提出了预诊断技术研究的发展趋势及各子问题的研究侧重点。 并对利用时间序列数据挖掘这一理论方法解决机械设备状态预诊断问题的可行性 进行了分析。 2. 针对具有波动频繁、噪声干扰严重等特点的原始振动量时间序列无法直接 用于旋转机械性能状态分析的问题，结合全息诊断信息融合分析旋转机械振动全 貌的思想，提出了全息状态矩阵的概念并给出定义，用类时间轴上的多维序列表 征转子系统振动全貌，以实现振动量时间序列的高级表示，为后续预测与匹配分 类工作提供良好的数据源，同时增强全息诊断的信息检索和知识自动获取的能力。 3. 将旋转机械性能状态预测，归结为旋转机械设备维护应用背景下的一维数 值型时间序列预测问题来进行深入研究。针对现有预测方法长期预测能力较弱， 且自动化水平低的不足，提出了用于旋转机械性能状态预测的ARIMA 动态间隔预 测法。该方法以动态间隔获取时间序列样本建模并预测的策略，提高了ARIMA 模 型用于设备状态长期预测的准确性，并且能够实现建模与预测的自动化，满足CBM 系统的实时性要求。 4. 针对全息状态矩阵表示的旋转机械性能状态特征数据，提出了一种全息状 态矩阵相似性匹配方法。结合旋转机械预诊断领域应用的特点定义了全息状态矩 阵的相似性度量模型，基于全息状态矩阵近似距离三角不等式设计了剪枝搜索策 略，并在此基础上设计了全息状态矩阵相似性高效准确匹配算法，不需要借助专家经验和人工识别确认，在一定阈值范围内能够实现高质量的旋转机械性能状态 相似性匹配。 5. 旋转机械基本振动量特征时间序列具有海量、超高维度、短期波动频繁和 大量噪声等特征，与时间序列数据挖掘传统应用的金融商业领域数据不同，直接 采用传统方法会存在搜索速度大幅度降低的问题。针对这一问题，提出了基于随 机投影的时间序列相似性搜索方法。该方法利用近年来新兴的随机投影统计学降 维法，将原始时间序列集映射到低维空间，并利用R*树进行索引，能够在保持高 准确率的同时，实现旋转机械基本振动量特征时间序列相似性快速搜索。 6. 针对现有机械设备性能状态分类方法不考虑误分类代价的问题，提出了一 种代价敏感直推式旋转机械设备性能状态分类法。该方法将代价敏感分类和直推 式学习的基本思想和理论相结合，采用一种代价敏感的直推式分类机制，实现了 机械设备性能状态的代价敏感分类。该方法在保证较高分类准确率的基础上，明 显地降低了误分类总代价。 7. 基于CBM 的基本理念，设计了旋转机械CBM 系统的基本结构，并以本 文理论方法的研究成果为核心，详细设计了各模块的基本功能和处理逻辑，采用 VC#.net 与Matlab 混合编程的方式开发了一个面向大型旋转机械的CBM 系统原 型，以验证本文机械设备预诊断方法研究成果的可操作性和实用性，为CBM 系统 应用技术研究做出了有益的探索。

Simulação, em software adequado, da sintomatologia vibracional de avarias mecânicas de máquinas rotativas

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica na Área de Manutenção e Produção

Estágio na empresa SPECMAN para a prática de técnicas de controlo da condição

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

Análise por ultra-sons no controlo de condição

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

Metodologia para otimização da manutenção

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia da Manutenção

Redes neurais artificiais aplicadas à manutenção baseada na condição

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Efficiency improvement of non-uniformly-aged PV arrays

The utilization of solar energy by photovoltaic (PV) systems have received much research and development (R&D) attention across the globe. In the past decades, a large number of PV array have been installed. Since the installed PV arrays often operate in harsh environments, non-uniform aging can occur and impact adversely on the performance of PV systems, especially in the middle and late periods of their service life. Due to the high cost of replacing aged PV modules by new modules, it is appealing to improve energy efficiency of aged PV systems. For this purpose, this paper presents a PV module reconfiguration strategy to achieve the maximum power generation from non-uniformly aged PV arrays without significant investment. The proposed reconfiguration strategy is based on the cell-unit structure of PV modules, the operating voltage limit of gird-connected converter, and the resulted bucket-effect of the maximum short circuit current. The objectives are to analyze all the potential reorganization options of the PV modules, find the maximum power point and express it in a proposition. This proposition is further developed into a novel implementable algorithm to calculate the maximum power generation and the corresponding reconfiguration of the PV modules. The immediate benefits from this reconfiguration are the increased total power output and maximum power point voltage information for global maximum power point tracking (MPPT). A PV array simulation model is used to illustrate the proposed method under three different cases. Furthermore, an experimental rig is built to verify the effectiveness of the proposed method. The proposed method will open an effective approach for condition-based maintenance of emerging aging PV arrays.