A review on machinery diagnostics and prognostics implementing condition-based maintenance

Condition-based maintenance (CBM) is a maintenance program that recommends maintenance decisions based on the information collected through condition monitoring. It consists of three main steps: data acquisition, data processing and maintenance decision-making. Diagnostics and prognostics are two important aspects of a CBM program. Research in the CBM area grows rapidly. Hundreds of papers in this area, including theory and practical applications, appear every year in academic journals, conference proceedings and technical reports. This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing CBM with emphasis on models, algorithms and technologies for data processing and maintenance decision-making. Realising the increasing trend of using multiple sensors in condition monitoring, the authors also discuss different techniques for multiple sensor data fusion. The paper concludes with a brief discussion on current practices and possible future trends of CBM.     

旋转机械机组群状态监测与故障诊断系统关键技术研究

研制开发了一套针对旋转机械机组群的状态监测与故障诊断系统,充分考虑了实际应用中环境的复杂性,采用的针对性措施保证了系统较高的适用性。所开发的系统进行了实验室验证。实验结果表明,系统达到了机组群状态监测与故障诊断的要求。     

Laser based measurement for the monitoring of shaft misalignment

This paper presents a method for real-time online monitoring of shaft misalignment, which is a common problem in rotating machinery, such as the drive train of wind turbines. A non-contact laser based measurement method is used to monitor positional changes of a rotating shaft in real time while in operation. The results are then used to detect the presence of shaft misalignment. An experimental test rig is designed to measure shaft misalignment and the results from the work show that the technique can be used for the monitoring of both offset and angular shaft misalignment, which will have applications in the condition monitoring and maintenance of various types of rotating machinery.     

基于VxWorks的旋转机械状态监测系统

旋转机械状态监测对实时性和可靠性具有较高的要求,因此基于VxWorks嵌入式实时操作系统以ARM9处理器为核心设计了一种水轮机的状态监测系统.文章介绍了系统的主体硬件结构和软件设计,并对在VxWorks操作系统下基于ARM9处理器的应用程序开发如系统任务划分、网络通信程序设计等进行了重点描述.该系统目前已通过调试仿真,并具有在其他旋转机械中推广应用的潜力.     

特种车辆油气弹簧漏气故障的识别与预测

针对特种车辆实现基于状态的维修和自主保障需求,首次对特种车辆悬挂系统的核心部件油气弹簧进行了基于数据驱动方法的状态识别和故障预测研究。对油气弹簧的主要故障模式和机理进行了分析,针对发生频率最高的漏气故障,提出了一种适用于不同工况下的基于相同位移下气压变化的特征提取方法。提出了一种基于支持向量分类机和回归机的故障识别和预测框架,采用该框架能适应实车应用环境的要求,无需增加额外传感器,只利用车辆现有的测试环境就能实现油气弹簧漏气故障的实时监测和预防性维护,与其他基于振动信号的方法相比更具有实用性。在试验室内进行了模拟状态退化试验,利用采集的数据验证了方法的可行性,可用于特种车辆油气悬架的在线状态监控和预测。     

基于组态思想的旋转机械状态监测系统

为适应旋转机械状态监测系统多样化需求,提出一种新颖的基于组态思想的旋转机械状态监测系统。该系统以配置研究设计的组态化状态监测系统管理分析软件平台的通用计算机为核心,加上研制的电涡流信号采集前置分机、压电信号采集前置分机、工艺量信号采集前置分机和系统组合控制分机组成。系统可以依据工业现场状态监测的实际需要,由用户方便地组建成具有多种信号采集和数据分析功能的离线的便携式振动数据采集分析系统、准在线状态监测系统、或在线状态监测系统。所有信号采集前置分机和系统组合控制分机都是以ＤＳ８０Ｃ３２０高速处理单片机为核心设计的高速、小型化、低功耗系统。论述系统总体方案、系统分机设计和管理分析软件设计,并给出系统在工业现场的应用结果。     

Design and realization of a remote monitoring and diagnosis and prediction system for large rotating machinery

Traditional on-site fault diagnosis means cannot meet the needs of large rotating machinery for its performance and complexity. Remote monitoring and diagnosis technology is a new fault diagnosis mode combining computer technology, communication technology, and fault diagnosis technology. The designed remote monitoring and diagnosis and prediction system for large rotating machinery integrates the distributed resources in different places and breaks through shortcomings as the offline and decentralized information. The system can make further implementation of equipment prediction technology research based on condition monitoring and fault diagnosis, provide on-site analysis results, and carry out online actual verification of the results. The system monitors real-time condition of the equipment and achieves early fault prediction with great significance to guarantee safe operation, saves maintenance costs, and improves utilization and management of the equipment.     

Integrated Equipment Health Prognosis Considering Crack Initiation Time Uncertainty and Random Shock

With integrated equipment health prognosis, both physical models and condition monitoring data are utilized to achieve more accurate prediction of equipment remaining useful life (RUL). In this paper, an integrated prognostics method is proposed to account for two important factors which were not considered before, the uncertainty in crack initiation time (CIT) and the shock in the degradation. Prognostics tools are used for RUL prediction starting from the CIT. However, there is uncertainty in CIT due to the limited capability of existing fault detection tools, and such uncertainty has not been explicitly considered in the literature for integrated prognosis. A shock causes a sudden damage increase and creates a jump in the degradation path, which shortens the total lifetime, and it has not been considered before in the integrated prognostics framework either. In the proposed integrated prognostics method, CIT is considered as an uncertain parameter, which is updated using condition monitoring data. To deal with the sudden damage increase and reduction of total lifetime, a virtual gradual degradation path with an earlier CIT is introduced in the proposed method. In this way, the effect of shock is captured through identifying an appropriate CIT. Examples of gear prognostics are given to demonstrate the effectiveness of the proposed method.     

Condition monitoring scheme via one-class support vector machine and multivariate control charts

A condition-based maintenance (CBM) has been widely employed to reduce maintenance cost by predicting the health status of many complex systems in prognostics and health management (PHM) framework. Recently, multivariate control charts used in statistical process control (SPC) have been actively introduced as monitoring technology. In this paper, we propose a condition monitoring scheme to monitor the health status of the system of interest. In our condition monitoring scheme, we first define reference data set using one-class support vector machine (OC-SVM) to construct the control limit of multivariate control charts in phase I. Then, parametric control chart or non-parametric control chart is selected according to the results from multivariate normality tests. The proposed condition monitoring scheme is applied to sensor data of two anemometers to evaluate the performance of fault detection power.

     

一种军用工程车辆车载状态监测系统设计

以军用工程车辆中挖掘机为研究对象,根据其工作原理、结构、使用、保养、维修情况,进行监测系统的总体设计,并以ARM微控制器为核心,应用嵌入式技术完成了信号采集及处理、液晶显示、数据存储等模块硬件设计,在KeilC开发环境下进行了系统软件开发,具有实时性好、可靠性高、监测精度高、系统扩展方便等特点,提高了军用工程车辆的在线故障检测与现场快速维修保障能力。     

旋转机械状态监测信号处理与数据管理系统的研制

状态监测中的两大核心技术是信号分析和信息管理,本文给出的数字信号处理软件包覆盖了几乎所 有的常规信号分析方法,且新的信号分析算法也可以方便地添加进去,针对机械状态监测中的几类主要数据,具体 地给出了组织、存储方法。     

Towards bearing failure prognostics: a practical comparison between data-driven methods for industrial applications

Research studies on data-driven approaches to rotating components and rolling element bearing (REB) prognostics have recently witnessed a rapid increase. These data-driven methods rely on sensor data for condition monitoring and degradation assessments; however, the problem of mining features from these sophisticated data using appropriate intelligent methods and choosing a practically reliable predictive model(s) has become a global concern. Vibration monitoring for REBs have over the years shown great effectiveness. Although monotonic statistical features serve as reliable health indicators (HIs), relying on a single feature for optimal bearing degradation assessment is inefficient. By fusing highly monotonic features using appropriate methods, a more reliable HI can be constructed and from this, various degradation states/stages and time to start prediction (TSP) can be identified by mapping known failure modes/degradation states to cluster points from clustering algorithms. Emphatically, the choice of regression algorithms for prognostics poses more concern as engineers and data scientists are faced with choosing between Bayessian machine learning (ML) and deep learning (DL) methods. This study presents a methodology for constructing a reliable HI for bearing prognostics, choosing a reliable TSP, and provides a comparison between ML and DL methods for bearing prognostics. As representatives of both domains, the Gaussian process regression (GPR) and the deep belief network (DBN) are introduced and compared. The results provide a reliable paradigm for prognosible feature representation for REBs and for choosing between both domains while considering their dependencies, efficiencies, and deficiencies.

     

STOCHASTIC PROGNOSTICS FOR ROLLING ELEMENT BEARINGS

The capability to accurately predict the remaining life of a rolling element bearing is prerequisite to the optimal maintenance of rotating machinery performance in terms of cost and productivity. Due to the probabilistic nature of bearing integrity and operation condition, reliable estimation of a bearing's remaining life presents a challenging aspect in the area of maintenance optimisation and catastrophic failure avoidance. Previous study has developed an adaptive prognostic methodology to estimate the rate of bearing defect growth based on a deterministic defect-propagation model. However, deterministic models are inadequate in addressing the stochastic nature of defect-propagation. In this paper, a stochastic defect-propagation model is established by instituting a lognormal random variable in a deterministic defect-propagation rate model. The resulting stochastic model is calibrated on-line by a recursive least-squares (RLS) approach without the requirement of a priori knowledge on bearing characteristics. An augmented stochastic differential equation vector is developed with the consideration of model uncertainties, parameter estimation errors, and diagnostic model inaccuracies. It involves two ordinary differential equations for the first and second moments of its random variables. Solving the two equations gives the mean path of defect propagation and its dispersion at any instance. This approach is suitable for on-line monitoring, remaining life prediction, and decision making for optimal maintenance scheduling. The methodology has been verified by numerical simulations and the experimental testing of bearing fatigue life.     

Intelligent condition-based prediction of machinery reliability

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models that attempt to forecast machinery health based on condition data. This paper presents a novel approach for incorporating population characteristics information and suspended condition trending data of historical units into prognosis. The population characteristics information extracted from statistical failure distribution enables longer-range prognosis. The accurate modelling of suspended data is also found to be of great importance, since in practice machines are rarely allowed to run to failure and hence data are commonly suspended. The proposed model consists of a feed-forward neural network whose training targets are asset survival probabilities estimated using a variation of the Kaplan–Meier estimator and a degradation-based failure probability density function (PDF) estimator. The trained network is capable of estimating the future survival probabilities of an operating asset when a series of condition indices are inputted. The output survival probabilities collectively form an estimated survival curve. Pump vibration data were used for model validation. The proposed model was compared with two similar models that neglect suspended data, as well as with a conventional time series prediction model. The results support our hypothesis that the proposed model can predict more accurately and further ahead than similar methods that do not include population characteristics and/or suspended data in prognosis.     

旋转机械信号采集中的一种脉冲剔除方法

脉冲干扰出现在旋转机械信号采集中可导致信号频谱的不稳定,直接影响了状态监测的准确性与可靠性。本文提出了一种简单有效的脉冲剔除方法,实验证明采用这种方法可成功地抑制脉冲干扰,提高信噪比。     

An intelligent approach to machine component health prognostics by utilizing only truncated histories

Numerous techniques and methods have been proposed to reduce the production downtime, spare-part inventory, maintenance cost, and safety hazards of machineries and equipment. Prognostics are regarded as a significant and promising tool for achieving these benefits for machine maintenance. However, prognostic models, particularly probabilistic-based methods, require a large number of failure instances. In practice, engineering assets are rarely being permitted to run to failure. Many studies have reported valuable models and methods that engage in maximizing both truncated and failure data. However, limited studies have focused on cases where only truncated data are available, which is common in machine condition monitoring. Therefore, this study develops an intelligent machine component prognostics system by utilizing only truncated histories. First, the truncated Minimum Quantization Error (MQE) histories were obtained by Self-organizing Map network after feature extraction. The chaos-based parallel multilayer perceptron network and polynomial fitting for residual errors were adopted to generate the predicted MQEs and failure times following the truncation times. The feed-forward neural network (FFNN) was trained with inputs both from the truncated MQE histories and from the predicted MQEs. The target vectors of survival probabilities were estimated by intelligent product limit estimator using the truncation times and generated failure times. After validation, the FFNN was applied to predict the machine component health of individual units. To validate the proposed method, two cases were considered by using the degradation data generated by bearing testing rig. Results demonstrate that the proposed method is a promising intelligent prognostics approach for machine component health.     

压缩机状态实时监测系统的研制

以压缩机为监测对象提出了由数据采集模块、状态监测模块和故障诊断模块组成的分布式信息处理系统的构思 ,阐明了基于结构层次化和功能模块化的状态监测系统的软、硬件设计方案。整个系统具有高速度、高精度、多通道、大容量和高性能价格比等特点 ,可广泛应用于各类大型旋转机械的状态监测。     

The diagnostic line: A novel criterion for condition monitoring of rotating machinery

This study examined scaling properties of an increment series from rotating machinery. Moreover, two fluctuation parameters for the smallest and largest time scales of a scaling range served as a pair of fluctuation parameters to describe system conditions. Therefore, an interesting phenomenon is observed: the data points, each representing a pair of fluctuation parameters, for fault conditions almost form a straight line, while those for normal clearly depart from the straight line. To describe the phenomenon, a novel concept termed the diagnostic line was introduced. Subsequently, properties of the diagnostic line were carefully investigated theoretically and numerically. Consequently, a decisive role of noise in forming the diagnostic line was determined. Accordingly, this study develops a novel criterion for condition monitoring of rotating machinery.     

旋转机械振动信号压缩小波基优化选取方法

旋转机械在线状态监测和故障诊断系统中振动数据的实时存储和远程传输对数据压缩提出了较高的要求.小波分析做为数据处理的常用方法,已被广泛地应用于数据压缩并取得了良好的效果.给出了振动信号的小波通用压缩方法,通过分析旋转机械振动信号的特点和小波函数几种重要属性对小波压缩的影响,提出了旋转机械振动信号压缩过程中最优小波基的选择方法,并根据旋转机械实际故障信号比较了相应小波的压缩效果.结果表明,通过选择合适的小波基函数,可以有效提高重构信号的信噪比.对于旋转机械复杂突变类故障信号应选择低分解消失矩,高重构正则性的双正交小波进行压缩.本研究方法和结论对旋转机械振动信号小波压缩的进一步研究有一定的参考作用.