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.

     

Rotating machinery prognostics: State of the art,challenges and opportunities

Machinery prognosis is the forecast of the remaining operational life, future condition, or probability of reliable operation of an equipment based on the acquired condition monitoring data. This approach to modern maintenance practice promises to reduce downtime, spares inventory, maintenance costs, and safety hazards. Given the significance of prognostics capabilities and the maturity of condition monitoring technology, there have been an increasing number of publications on rotating machinery prognostics in the past few years. These publications covered a wide spectrum of prognostics techniques. This review article first synthesises and places these individual pieces of information in context, while identifying their merits and weaknesses. It then discusses the identified challenges, and in doing so, alerts researchers to opportunities for conducting advanced research in the field. Current methods for predicting rotating machinery failures are summarised and classified as conventional reliability models, condition-based prognostics models and models integrating reliability and prognostics. Areas in need of development or improvement include the integration of condition monitoring and reliability, utilisation of incomplete trending data, consideration of effects from maintenance actions and variable operating conditions, derivation of the non-linear relationship between measured data and actual asset health, consideration of failure interactions, practicability of requirements and assumptions, as well as development of performance evaluation frameworks.     

Current status of machine prognostics in condition-based maintenance: a review

Condition-based maintenance (CBM) is a decision-making strategy based on real-time diagnosis of impending failures and prognosis of future equipment health. It is a proactive process that requires the development of a predictive model that can trigger the alarm for corresponding maintenance. Prognostic methodologies for CBM have only recently been introduced into the technical literature and become such a focus in the field of maintenance research and development. There are many research and development on a variety of technologies and algorithms that can be regarded as the steps toward prognostic maintenance. They are needed in order to support decision making and manage operational reliability. In this paper, recent literature that focuses on the machine prognostics has been reviewed. Generally, prognostic models can be classified into four categories: physical model, knowledge-based model, data-driven model, and combination model. Various techniques and algorithms have been developed depending on what models they usually adopt. Based on the review of some typical approaches and new introduced methods, advantages and disadvantages of these methodologies are discussed. From the literature review, some increasing trends appeared in the research field of machine prognostics are summarized. Furthermore, the future research directions have been explored.     

A prognosis method using age-dependent hidden semi-Markov model for equipment health prediction

Health monitoring and prognostics of equipment is a basic requirement for condition-based maintenance (CBM) in many application domains. This paper presents an age-dependent hidden semi-Markov model (HSMM) based prognosis method to predict equipment health. By using hazard function (h.f.), CBM is based on a failure rate which is a function of both the equipment age and the equipment conditions. The state values of the equipment condition considered in CBM, however, are limited to those stochastically increasing over time and those having non-decreasing effect on the hazard rate. The previous HSMM based prognosis algorithm assumed that the transition probabilities are only state-dependent, which means that the probability of making transition to a less healthy state does not increase with the age. In the proposed method, in order to characterize the deterioration of equipment, three types of aging factors that discount the probabilities of staying at current state while increasing the probabilities of transitions to less healthy states are integrated into the HSMM. With an iteration algorithm, the original transition matrix obtained from the HSMM can be renewed with aging factors. To predict the remaining useful life (RUL) of the equipment, hazard rate is introduced to combine with the health-state transition matrix. With the classification information obtained from the HSMM, which provides the current health state of the equipment, the new RUL computation algorithm could be applied for the equipment prognostics. The performances of the HSMMs with aging factors are compared by using historical data colleted from hydraulic pumps through a case study.     

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。     

浅谈测控设备实现CBM的难点与对策

分析了测控设备进行CBM(基于状态维修)中的状态监测、失效模型建立、故障预测和维修决策等关键点,讨论了其技术对策,以供测控设备实现CBM参考.     

基于状态监测和故障诊断的设备管理系统

当前由于状态监测和故障诊断与维修管理的相互脱节,使其应有的作用没有得到充分发挥。针对这种情况,采用了离线和在线监测诊断相结合的状态维修策略,建立了状态维修的工作模型,运用集成定义方法建立了系统的功能模型,进而综合采用网络技术、计算机技术和数据库技术等,开发出了基于状态监测和故障诊断的设备管理系统,为企业成功开展状态维修提供了有效的管理工具。     

Rotor fault condition monitoring techniques for squirrel-cage induction machine—A review

Nowadays, manufacturing companies are making great efforts to implement an effective machinery maintenance program, which provides incipient fault detection. The machine problem and its irregularity can be detected at an early stage by employing a suitable condition monitoring accompanied with powerful signal processing technique. Among various defects occurred in machines, rotor faults are of significant importance as they cause secondary failures that lead to a serious motor malfunction. Diagnosis of rotor failures has long been an important but complicated task in the area of motor faults detection. This paper intends to review and summarize the recent researches and developments performed in condition monitoring of the induction machine with the purpose of rotor faults detection. The aim of this article is to provide a broad outlook on rotor fault monitoring techniques for the researchers and engineers.     

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.     

A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology

Diagnostics and prognostics are two important aspects in a condition-based maintenance (CBM) program. However, these two tasks are often separately performed. For example, data might be collected and analysed separately for diagnosis and prognosis. This practice increases the cost and reduces the efficiency of CBM and may affect the accuracy of the diagnostic and prognostic results.In this paper, a statistical modelling methodology for performing both diagnosis and prognosis in a unified framework is presented. The methodology is developed based on segmental hidden semi-Markov models (HSMMs). An HSMM is a hidden Markov model (HMM) with temporal structures. Unlike HMM, an HSMM does not follow the unrealistic Markov chain assumption and therefore provides more powerful modelling and analysis capability for real problems. In addition, an HSMM allows modelling the time duration of the hidden states and therefore is capable of prognosis. To facilitate the computation in the proposed HSMM-based diagnostics and prognostics, new forward–backward variables are defined and a modified forward–backward algorithm is developed. The existing state duration estimation methods are inefficient because they require a huge storage and computational load. Therefore, a new approach is proposed for training HSMMs in which state duration probabilities are estimated on the lattice (or trellis) of observations and states. The model parameters are estimated through the modified forward–backward training algorithm. The estimated state duration probability distributions combined with state-changing point detection can be used to predict the useful remaining life of a system.The evaluation of the proposed methodology was carried out through a real world application: health monitoring of hydraulic pumps. In the tests, the recognition rates for all states are greater than 96%. For each individual pump, the recognition rate is increased by 29.3% in comparison with HMMs. Because of the temporal structures, the same HSMMs can be used to predict the remaining-useful-life (RUL) of the pumps.     

基于模糊层次分析法的维修方式群体决策模型

在分析现有各种维修决策方法及维修决策特点的基础上,提出基于模糊层次分析法的维修方式群体决策模型,利用模糊有序加权几何平均算子(fuzzy ordered weighted geometric operator,FOWG算子),得到模糊判断矩阵的精确权重。将该方法运用于热电厂发电设备维修方式决策,最终从三种备选方案中(故障后维修、计划维修、视情维修)选出视情维修作为维修方式。最后指出该方法的优点、不足之处及其发展方向。     

Remaining Useful Life Model and Assessment of Mechanical Products: A Brief Review and a Note on the State Space Model Method

The remaining useful life(RUL) prediction of mechanical products has been widely studied for online system performance reliability, device remanufacturing, and product safety(safety awareness and safety improvement). These studies incorporated many di erent models, algorithms, and techniques for modeling and assessment. In this paper, methods of RUL assessment are summarized and expounded upon using two major methods: physics model based and data driven based methods. The advantages and disadvantages of each of these methods are deliberated and compared as well. Due to the intricacy of failure mechanism in system, and di culty in physics degradation observation, RUL assessment based on observations of performance variables turns into a science in evaluating the degradation. A modeling method from control systems, the state space model(SSM), as a first order hidden Markov, is presented. In the context of non-linear and non-Gaussian systems, the SSM methodology is capable of performing remaining life assessment by using Bayesian estimation(sequential Monte Carlo). Being e ective for non-linear and non-Gaussian dynamics, the methodology can perform the assessment recursively online for applications in CBM(condition based maintenance), PHM(prognostics and health management), remanufacturing, and system performance reliability. Finally, the discussion raises concerns regarding online sensing data for SSM modeling and assessment of RUL.     

过程工业动态的以可靠性为中心的维修研究及应用

针对过程工业设备类型、结构、运行和维护特点,研究过程工业以可靠性为中心的维修评估技术,开发集风险评估、状态监测、绩效审查、专业管理、档案管理为一体的动态的以可靠性为中心的维修决策系统。利用风险评估技术确定设备优先维护等级、优化维修内容、确定最佳维修时间指导设备维护和维修决策;状态监测用于故障诊断与预测确定设备预知性维修任务;绩效指标用于量化关键部件故障频率和故障后果,确定关键部件的最佳维修周期进而量化设备动态风险、确定预防性维修任务内容;专业管理是维修决策和维修内容的执行环节。开发的系统应用于中国石油某炼油厂,实现以信息为重要资源,以设备健康状态为依据,基于风险量化指标决策和设备量化风险动态管理,提高维修决策水平,确保了设备运行的安全性、可靠性和可用性。     

基于CBM策略的航空发动机在翼寿命预测研究

为了给航空公司制定航空发动机拆换计划提供科学合理的依据,基于视情维修方式,对航空发动机的在翼寿命进行了研究。首先,根据发动机历史拆换记录,基于比例危险-比例优势模型建立了发动机维修决策控制限的数学模型。然后,利用基于最小二乘支持向量机的时间序列预测方法对发动机状态参数进行了趋势预测,进而结合维修决策控制限模型便可得到发动机在翼寿命的预测结果。最后通过实例验证了该方法的有效性和实用价值。     

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.     

Condition-based spares ordering for critical components

It is widely accepted that one of the potential benefits of condition-based maintenance (CBM) is the expected decrease in inventory as the procurement of parts can be triggered by the identification of a potential failure. For this to be possible, the interval between the identification of the potential failure and the occurrence of a functional failure (P-F interval) needs to be longer than the lead time for the required part. In this paper we present a model directed to the determination of the ordering decision for a spare part when the component in operation is subject to a condition monitoring program. In our model the ordering decision depends on the remaining useful life (RUL) estimation obtained through (i) the assessment of component age and (ii) condition indicators (covariates) that are indicative of the state of health of the component, at every inspection time. We consider a random lead time for spares, and a single-component, single-spare configuration that is not uncommon for very expensive and highly critical equipment.     

Optimization of dimensionality of symptom space in machine condition monitoring

With the modern tools of metrology we can measure almost all variables in the phenomenon field of a working machine, and some of measuring quantities can be symptoms of machine condition. On this basis we can form the symptom observation matrix for condition monitoring. From the other side we know that contemporary complex machines can have many modes of failure/damage, so called faults. The paper presents the method of extraction of fault information from the symptom observation matrix by means of singular value decomposition, in the form of generalized fault symptoms. However, at the beginning of monitoring we do not know the sensitivity of potential symptoms to the given machine faults and to its overall condition. Hence, some method of symptom observation matrix optimization leading to redundancy minimization is presented first time in this paper. This gives the possibility to assess the diagnostic contribution of every primary measured symptom. Also in the paper some possibility to assess symptom limit value, based on symptom reliability is considered. These concepts are illustrated by symptom observation matrix processing with the special program and the data are taken directly from the machine vibration condition monitoring area.     

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.

     

复杂系统故障预测与综合健康管理技术的研究

对于目前很多大型复杂的系统来说,故障预测以及综合健康管理技术方面的研究越来越受到重视。文章对故障预测相关技术作了论述,就复杂系统的综合健康管理（IHM）的组成及关键技术进行了阐述,并介绍了一种视情维修开放体系结构。