Capacitors are widely used in dc links of power electronic converters to balance power, suppress voltage ripple, and store short-term energy. Condition monitoring (CM) of dc-link capacitors has great ...significance in enhancing the reliability of power converter systems. Over the past few years, many efforts have been made to realize CM of dc-link capacitors. This article gives an overview and a comprehensive comparative evaluation of them with emphasis on the application objectives, implementation methods, and monitoring accuracy when being used. First, the design procedure for the CM of capacitors is introduced. Second, the main capacitor parameters estimation principles are summarized. According to these principles, various possible CM methods are derived in a step-by-step manner. On this basis, a comprehensive review and comparison of CM schemes for different types of dc-link applications are provided. Finally, application recommendations and future research trends are presented.
•An extended and up-to-date review on bearing fault assessment (BFA) is provided.•Detailed information on signal processing and learning approaches is given.•Fault size and damage degradation ...estimation are stated as the main aims in BFA.•VIB, AE, CUR and VOLT are found as the main signals to extract useful information.•Open problems and challenges on this research topic are discussed.
Health condition monitoring of rotating machinery is a crucial task to guarantee reliability in industrial processes. In particular, bearings are mechanical components used in most rotating devices and they represent the main source of faults in such equipments; reason for which research activities on detecting and diagnosing their faults have increased. Fault detection aims at identifying whether the device is or not in a fault condition, and diagnosis is commonly oriented towards identifying the fault mode of the device, after detection. An important step after fault detection and diagnosis is the analysis of the magnitude or the degradation level of the fault, because this represents a support to the decision-making process in condition based-maintenance. However, no extensive works are devoted to analyse this problem, or some works tackle it from the fault diagnosis point of view. In a rough manner, fault severity is associated with the magnitude of the fault. In bearings, fault severity can be related to the physical size of fault or a general degradation of the component. Due to literature regarding the severity assessment of bearing damages is limited, this paper aims at discussing the recent methods and techniques used to achieve the fault severity evaluation in the main components of the rolling bearings, such as inner race, outer race, and ball. The review is mainly focused on data-driven approaches such as signal processing for extracting the proper fault signatures associated with the damage degradation, and learning approaches that are used to identify degradation patterns with regards to health conditions. Finally, new challenges are highlighted in order to develop new contributions in this field.
Intelligent spindles are core components of the next-generation of intelligent/smart machine tools in the Industry 4.0 Era. The purpose of this paper is to clarify the concept of intelligent spindles ...and provide an in-depth review of the state-of-the-art of related technologies. A new integrated concept for intelligent spindles is proposed, followed by descriptions of required characteristics, key enabling technologies and expected intelligent functions. Relevant research that may be beneficial to the development of intelligent spindles is reviewed from six thrust areas, which include monitoring and control of tool condition, chatter, spindle collision, temperature/thermal error, spindle balance, and spindle health. Finally, current limitations and challenges are discussed, and future trends of intelligent spindles are prospected from various perspectives.
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•A new integrated concept for intelligent spindles is proposed.•Previous research works related with intelligent spindles are reviewed.•Future trends of intelligent spindles are prospected.
On-line wear debris monitoring is a useful technology for real-time machine wear condition monitoring but needs further development. This study, based on previous developments of an on-line visual ...ferrograph (OLVF), focused on (i) data reconstruction for extracting representative and reliable wear condition related characteristics, and (ii) development of an improved model for on-line wear prediction. Wear monitoring of a diesel engine was performed using this on-line wear debris monitoring system. Experimental results and comparisons between the improved relevance vector machine (RVM) model and other models show that the improved RVM model gives an earlier warning and enhances the prediction accuracy.
Tool condition monitoring is critical in ultraprecision manufacturing in order to optimize the performance of the overall process, while maintaining the desired part quality. Recently, deep learning ...has been successfully applied to numerous classification tasks in manufacturing, often to forecast part quality. In this paper, a novel deep learning data-driven modeling framework is presented, which includes a fusion of multiple stacked sparse autoencoders for tool condition monitoring in ultraprecision machining. The proposed computational framework consists of two main structures. First, a training model that is designed with the ability to process multiple parallel feature spaces to learn the lower-level features. Second, a feature fusion structure that is used to learn the higher-level features and associations to tool wear. To achieve this learning structure, a modified loss function is utilized that enhances the feature extraction and classification tasks. A dataset from a real manufacturing process is used to demonstrate the performance of the proposed framework. Experimental results and simulations show that the proposed method successfully classifies the ultraprecision machining case study with over 96% accuracy, while also outperforming comparable methodologies.
The future of risk assessment Zio, E.
Reliability engineering & system safety,
September 2018, 2018-09-00, 20180901, 2018-09, Letnik:
177
Journal Article
Recenzirano
Odprti dostop
•A view on the future of risk assessment is provided.•Research directions are presented on the use of simulation for accident scenario identification and exploration.•The use of data for condition ...monitoring-based, dynamic risk assessment is discussed.•The extension of risk assessment into the framework of resilience and business continuity is presented.•The directions for and integrated safety and security assessment of CPSs are discussed.
Risk assessment must evolve for addressing the existing and future challenges, and considering the new systems and innovations that have already arrived in our lives and that are coming ahead. In this paper, I swing on the rapid changes and innovations that the World that we live in is experiencing, and analyze them with respect to the challenges that these pose to the field of risk assessment. Digitalization brings opportunities but with it comes also the complexity of cyber-phyiscal systems. Climate change and extreme natural events are increasingly threatening our infrastructures; terrorist and malevolent threats are posing severe concerns for the security of our systems and lives. These sources of hazard are extremely uncertain and, thus, difficult to describe and model quantitatively.
Some research and development directions that are emerging are presented and discussed, also considering the ever increasing computational capabilities and data availability. These include the use of simulation for accident scenario identification and exploration, the extension of risk assessment into the framework of resilience and business continuity, the reliance on data for dynamic and condition monitoring-based risk assessment, the safety and security assessment of cyber-physical systems.
The paper is not a research work and not exactly a review or a state of the art work, but rather it offers a lookout on risk assessment, open to consideration and discussion, as it cannot pretend to give an absolute point of view nor to be complete in the issues addressed (and the related literature referenced to).
•Failure modes of large-scale wind turbine bearings are reviewed.•Condition monitoring and fault diagnosis methods are reviewed and summarized.•Future research directions are discussed.
Large-scale ...wind turbine bearings including main bearings, gearbox bearings, generator bearings, blade bearings and yaw bearings, are critical components for wind turbines to convert kinetic wind energy into electrical energy. Unlike general-purpose industrial bearings, the loads and rotation speeds of wind turbine bearings change considerably because of dynamic wind flows. In the case of some extreme operating conditions, large-scale wind turbine bearings suffer excessive loads and they can be potentially damaged. Therefore, it is essential to develop reliable and cost-effective condition monitoring and fault diagnosis methods to assess the damage level and failure modes so that a proper maintenance plan can be designed. This paper aims at systematically and comprehensively summarizing current large-scale wind turbine bearing failure modes and condition monitoring and fault diagnosis achievements. Firstly, the representative failure modes of large-scale wind turbine bearings are reviewed in detail which can help to understand the causes and effects of the tribological issues of these bearings. Then, condition monitoring and fault diagnosis methods of large-scale wind turbine bearings are presented; within which failure modes, experimental scale and signal processing approaches are summarized. Finally, a number of popular condition monitoring and fault diagnosis approaches that can be potentially used for wind turbine bearings are reviewed, followed by a brief summary of future research directions for wind turbine bearing fault diagnosis.
Tool wear is one of the important indicators to reflect the health status of a machining system. In order to obtain tool’s wear status, tool condition monitoring (TCM) utilizes advanced sensor ...techniques, hoping to find out the wear status through those sensor signals. In this paper, a novel weighted hidden Markov model (HMM)-based approach is proposed for tool wear monitoring and tool life prediction, using the signals provided by TCM techniques. To describe the dynamic nature of wear evolution, a weighted HMM is first developed, which takes wear rate as the hidden state and formulates multiple HMMs in a weighted manner to include sufficient historical information. Explicit formulas to estimate the model parameters are also provided. Then, a particular probabilistic approach using the weighted HMM is proposed to estimate tool wear and predict tool’s remaining useful life during tool operation. The proposed weighted HMM-based approach is tested on a real dataset of a high-speed CNC milling machine cutters. The experimental results show that this approach is effective in estimating tool wear and predicting tool life, and it outperforms the conventional HMM approach.
Missing values are a common occurrence in condition monitoring datasets. To effectively improve the integrity of data, many data imputation methods have been developed to replace the missing values ...with the estimated values. However, these methods do not always perform well in datasets containing different types of missing values. Three types of missing data are defined, namely isolated missing value, continuous missing variable, and continuous missing sample. A three-step data imputation method is proposed to sequentially impute these missing values following the principle from easy to difficult. The original time series data is first to split into different segments according to the positions of continuous missing samples. Then, interpolation and space-based methods are applied to sequentially estimate isolated missing values and continuous missing variables in each segment. Finally, a stepwise extrapolation prediction model based on the long short-term memory network is established to repair continuous missing samples between each segment. Two application examples are implemented on different dissolved gas analysis datasets and load datasets. Compared with state-of-the-art techniques, the proposed three-step data imputation method is general and can be applied to many scenarios because it establishes a rational data recovery sequence to accurately repair both stationary and non-stationary condition monitoring data.
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