笼型异步电动机状态监测与转子故障诊断方法对比分析

笼型异步电动机广泛应用到工农业生产领域,转子故障是其主要的故障形式.本文针对笼型异步电动机转子故障的几种诊断方法,分析了他们各自的优缺点,阐明了检测异步电动机转子故障的关键所在,并针对异步电动机转子故障诊断的复杂性提出了相应的对策.     

基于效率函数求解单件车间调度问题算法的软件实现和实例分析

单件小批量生产形式下的单件车间（Job-shop）调度是生产计划中的一个重要问题。西方在文献[1]提出的求解Job-shop调度问题的初排算法及调解算法的基础上，做出了进一步的修改和完善，在调解算法中引入了交换与移动相结合的机制以提高调解效率；在软件实现中引入了“虚工序”的概念，并在“VBA ForProject2000中实现了该算法。经分析及实验验证，利用这种算法求解Job-shop调度问题可得到十分满意的结果。     

基于状态监测的全数字预测倍频技术研究

随着信息技术的迅速发展,在电力、石化等众多行业里得到广泛的重视和应用的旋转机械状态监测技术面临着巨大的发展和挑战。对振动信号的采集与分析是设备状态监测和故障诊断的基础,本文在对传统数字倍频器的分析基础上,结合现场实际情况设计出一种带预测功能的全数字倍频器,该项技术在状态监测系统中得到了很好的应用。     

纺织厂组合式空调机组智能模糊控制系统的开发

以化纤厂纺丝车间侧吹风为例，介绍了应用９０年代工控技术开发研制的纺织厂及化纤厂空调系统用组合式空调机且的智能模糊控制系统。     

瞬态工况对内燃机燃烧噪声的影响研究

研究了内燃机瞬态工况对燃烧噪声影响机理,开展了内燃机瞬态工况测试技术和测试方法研究,通过对瞬态与稳态过程的缸内压力、缸内压力最大值、压力升高率、压力升高率最大值、压力高频振荡以及气缸压力级等参数在不同频率范围的差异的研究分析,从气体动力载荷和高频压力振荡两方面分析研究瞬态噪声与稳态噪声产生差异的机理.瞬态与稳态工况压力升高率变化趋势与两种工况下燃烧噪声变化趋势基本相似,但在一些工况上存在差异,这是由于受到了缸内压力高频振荡对燃烧噪声影响的结果.结果表明：瞬态与稳态工况燃烧噪声的差异是由压力动力载荷与压力高频振荡共同影响的结果.     

状态检修技术及其辅助分析系统的应用

状态检修是近年来输变电企业普遍关注的技术。文中提出了一种基于模糊数学和专家系统的状态检修分析模型，它集成了各种信息和分析手段，弥补了一般电力企业在线监测和故障诊断手段缺乏的问题。文章同时阐述了绍兴电力局开发并已应用的状态检修计算机辅助分析系统的设计思路和主要模块。     

切屑控制的简化数学模型

在现代切削加工中，切削速度与金属切除率均已达到了很高水平，在切削条件很恶劣的情况下，就需要合理的设计槽型和精确的编制制造工艺，本在对切屑折断条件，断屑槽形的参数分析的基础上，建立了一个可靠而实用的数学模型，这对切屑控制的进一步研究具有一定的意义。     

Artificial neural networks and genetic algorithm for bearing fault detection

A study is presented to compare the performance of three types of artificial neural network (ANN), namely, multi layer perceptron (MLP), radial basis function (RBF) network and probabilistic neural network (PNN), for bearing fault detection. Features are extracted from time domain vibration signals, without and with preprocessing, of a rotating machine with normal and defective bearings. The extracted features are used as inputs to all three ANN classifiers: MLP, RBF and PNN for two- class (normal or fault) recognition. Genetic algorithms (GAs) have been used to select the characteristic parameters of the classifiers and the input features. For each trial, the ANNs are trained with a subset of the experimental data for known machine conditions. The ANNs are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a rotating machine. The roles of different vibration signals and preprocessing techniques are investigated. The results show the effectiveness of the features and the classifiers in detection of machine condition.     

Model-based and Fuzzy Logic Approaches to Condition Monitoring of Operational Wind Turbines

 It is common for wind turbines to be installed in remote locations on land or offshore, leading to difficulties in routine inspection and maintenance. Further, wind turbines in these locations are often subject to harsh operating conditions. These challenges mean there is a requirement for a high degree of maintenance. The data generated by monitoring systems can be used to obtain models of wind turbines operating under different conditions, and hence predict output signals based on known inputs. A model-based condition monitoring system can be implemented by comparing output data obtained from operational turbines with those predicted by the models, so as to detect changes that could be due to the presence of faults. This paper discusses several techniques for model-based condition monitoring systems: linear models, artificial neural networks, and state dependent parameter "pseudo" transfer functions. The models are identified using supervisory control and data acquisition (SCADA) data acquired from an operational wind firm. It is found that the multiple-input single-output state dependent parameter method outperforms both multivariate linear and artificial neural network-based approaches. Subsequently, state dependent parameter models are used to develop adaptive thresholds for critical output signals. In order to provide an early warning of a developing fault, it is necessary to interpret the amount by which the threshold is exceeded, together with the period of time over which this occurs. In this regard, a fuzzy logic-based inference system is proposed and demonstrated to be practically feasible.     

重组毕氏酵母表达rhs-TRAIL发酵条件的优化研究

为了提高重组人可溶性肿瘤坏死因子相关的凋亡诱导配体(rhs—TRAIL,114-281)在毕氏酵母工程菌中的表达量,采用1000mL摇瓶培养体系,优化研究了在不同培养基,pH值、表达时间、甲醇浓度等培养条件下的表达情况。经条件优化,最终确定了培养基为完全培养基BMGY／BMMY,甲醇浓度为0．5％,pH为6.0,表达时间为96h。在此优化条件下,目的蛋白表达量可以达到68mg/L。