压气站场作为天然气运输的动力心脏,其工艺流程和设备设施复杂繁多,亟需一套高效、直观、信息化的压气站场风险智能决策系统。为了提升压气站场的风险管控能力,融合数字孪生理念,基于风险的检验技术和以可靠性为中心的维修方法,采用现代化数字测量技术建立压气站场数字化模型,以传感通信技术为纽带构建实时信息的数字孪生体,研发了适用于压气站场设备设施的可视化风险分析系统。研究结果表明:（1）多尺度数字模型构建方法能够实现压气站场数字化模型的实景复制,以完成数字孪生理念中虚拟产品的构建;（2）结合先进的传感和数据读存技术能够实现压气站场及其设备设施设计参数、运行参数和环境参数等多源异构数据的整合,以实现信息数据与数字化压气站场模型的融合集成;（3）压气站场的静动设备风险分析评价应以完成基于风险的检验和以可靠性为中心的维修来实现压气站场设备设施减缓控制风险措施的制订;（4）计算机技术可以实现压气站场设备设施风险分析决策的可视化和自主化。结论认为,该研究成果可以整合压气站场的多源异构数据、提高压气站场的安全管理和信息化水平,有助于推动压气站场的智慧化建设。

全球制造业正在向着智能化、规模化的方向快速发展,但是数控设备之间存在数据接口不统一、设备数据交互困难、现场设备状态监测成本较高等难题。现提出一种基于数字孪生的数控设备互联互通及可视化技术,首先利用三维激光扫描技术构建车间数控设备数字孪生模型,通过OPC UA通讯构架读取数控设备的实时运行数据,并转化格式后存入数据库作为指导虚拟现实引擎的源数据,使用Unity数据驱动引擎去驱动数字孪生模型,从而实现物理模型与数字孪生模型的同步运动。最后,对车间数控设备的生产过程进行了数字孪生技术的实现,验证了所提方案的有效性和正确性,为面向互联互通的数字孪生数控设备提供了技术支持。

针对油气集输系统中在用的大型输油泵出现的效率低、能耗高等问题,需要对其进行现场性能测试和效率评估,以改善运行状况。通过调研国内外相关研究案例,重点围绕测试方法和评价指标进行了归纳和总结。研究发现,当前国内仍以传统方法为主,国外传统方法和热力学法应用均已比较普遍,其中,加拿大水泵测试项目不仅获得了统计效应级的测试数据,还提出了最具优势的泵效评价指标——PEI指标,极大地推进了热力学法在泵性能测试领域的应用进程。提出了一套综合性的输油泵性能评价指标体系,旨在为输油泵综合性能进行科学、全面的评价提供参考。

Predictive maintenance has raised much research interest to improve the system reliability of a wind turbine. This paper presents a new model based approach of integrated fault diagnosis and prognosis for wind turbine remaining useful life estimation, especially the cases with limited degradation data. Firstly, a wavelet transform based fault diagnosis method is investigated to analyze the bearing incipient defect signatures, and the extracted features are then fused by the Health Index algorithm to represent the bearing defect conditions. Taking the empirical physical knowledge and statistical model in a Bayesian framework, the bearing remaining useful life prediction with uncertainty quantification is achieved by particle filter in a recursive manner. The integrated fault diagnosis and prognosis approach is validated using bearing lifetime test data acquired from a wind turbine in field, and the performance comparison with typical data driven technique outlines the significance of the presented method. © 2019 Elsevier Ltd

为"挖掘"输油泵机组风险根源,降低设备预知性维护难度,结合输油泵多准则风险评价,提出1种基于等级全息建模的输油泵机组风险根源辨识方法,运用等级全息建模方法将输油泵系统分解为泵体结构、管理因素、环境因素、操作因素、技术因素、运行因素、设备安装7个子系统进行定性和定量分析。结果表明:相比危险与可操作性分析（HAZOP）、事故树分析（FTA）等传统风险辨识方法,等级全息建模（HHM）对轴承等关键部件以及压力等运行参数的监测更为深入,能够有效辨识输油泵机组高风险情景,提升输油泵的风险辨识效率。

为应对储气库分离器日益复杂的工艺流程和逐步大型化的工艺设备,采用基于风险检验（RBI）的技术进行储气库分离器设备的风险分析。通过对分离器设备参数、运行参数和所处环境参数等多源异构数据进行搜集集成,构建分离器失效模型,计算分离器的失效可能性和失效后果面积,划分分离器的风险等级,从而制定分离器的检验周期和检验措施;将分离器RBI风险分析方法与先进的传感技术和大数据分析技术相结合,研发计算机辅助软件。结果表明:通过对分离器运行数据进行实时获取与处理,可实现RBI风险分析结果的精确性和自动化,从而提高储气库站场的安全管理水平。

Induction motor is the main drive power in modern manufacturing, and timely fault diagnosis of induction motor is of significance to production safety, part quality and maintenance cost control. Data fusion-based diagnosis is attractive for effective utilization of multi-source monitoring information of motors with the development of industrial internet of things. A new multi-sensory fusion model is proposed, named dynamic routing-based multimodal neural network (DRMNN), following the paradigm of multimodal deep learning (MDL). Specifically, the fusion of vibration and stator current signals are investigated. A multimodal feature extraction scheme is designed for dimensionality reduction and invariant features capturing based on multi-source information. Since it is necessary to determine the importance of each modality, a dynamic routing algorithm is introduced in the decision layer to adaptively assign proper weights to different modalities. The effectiveness and robustness of developed DRMNN is demonstrated in the experimental studies performed on a motor test rig. In comparison with similar neural networks without data fusion and other state-of-art fusion techniques, the proposed DRMNN yields better performance.
© 2020 The Society of Manufacturing Engineers

The variability of machinery fault signatures causes the data samples to follow different distributions under various operating conditions, which poses significant challenges on autonomous diagnosis based on machine learning techniques. This article presents a new transfer learning method for cross-domain feature learning by mitigating the domain difference caused by various operating conditions for machinery autonomous diagnosis. More specifically, a factor analysis (FA)-based transfer learning method is formulated and named as probabilistic transfer FA (PTFA). It seeks a new feature space across different domains corresponding to various operating conditions and then transfers the original features into a low-dimensional latent space via feature extraction to minimize domain difference and preserve data properties. The learned features by PTFA minimize the domain difference and are then used to construct the machinery autonomous diagnosis model based on machine learning techniques [e.g., support vector machine (SVM)]. The effectiveness of the PTFA method is demonstrated in the experimental tests for a gearbox diagnosis under various operating conditions comparing with traditional feature extraction and transfer learning techniques. © 1963-2012 IEEE.