Yaguo Lei

Yaguo Lei
Xi'an Jiaotong University | XJTU ·  School of Mechanical Engineering

PhD

About

150
Publications
124,720
Reads
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17,952
Citations
Citations since 2017
78 Research Items
15709 Citations
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201720182019202020212022202301,0002,0003,0004,000
201720182019202020212022202301,0002,0003,0004,000
201720182019202020212022202301,0002,0003,0004,000
Additional affiliations
September 2012 - August 2013
University of Duisburg-Essen
Position
  • AvH Fellow
January 2010 - present
Xi'an Jiaotong University
Position
  • Professor (Full)
January 2008 - December 2009
University of Alberta
Position
  • PostDoc Position

Publications

Publications (150)
Article
Remaining useful life (RUL) prediction allows for predictive maintenance of machinery, thus reducing costly unscheduled maintenance. Therefore, RUL prediction of machinery appears to be a hot issue attracting more and more attention as well as being of great challenge. This paper proposes a model-based method for predicting RUL of machinery. The me...
Article
Tooth damage often causes a reduction in gear mesh stiffness. Thus time-varying mesh stiffness (TVMS) can be treated as an indication of gear health conditions. This study is devoted to investigating the mesh stiffness variations of a pair of external spur gears with tooth pitting, and proposes a new model for describing tooth pitting based on prob...
Article
Full-text available
This paper presents a novel method for fault diagnosis based on empirical mode decomposition (EMD), an improved distance evaluation technique and the combination of multiple adaptive neuro-fuzzy inference systems (ANFISs). The method consists of three stages. First, prior to feature extraction, some preprocessing techniques, like filtration, demodu...
Article
Based on the features extracted from the condition monitoring data, data-driven prognostic approaches are able to predict the remaining useful life (RUL) of machinery. Existing methods usually assume that a certain feature contributes consistently to the prediction results during the operation. In fact, the degradation sensitivity of each feature v...
Article
Industrial robots have become key components for manufacturing automations due to their larger workspaces and flexibility. However, low stiffness and high compliance of industrial robots may inevitably lead to vibration by self-excitation or periodic force dependent on workspace configuration. Therefore, the knowledge of the robot's modal propertie...
Chapter
The availability of industrial big data offers great opportunities as well as challenges for intelligent maintenance of mechanical systems. This chapter presents the general introduction to big data-driven fault diagnosis and prognosis for mechanical systems. Three popular maintenance strategies are briefly discussed first, which are followed by th...
Chapter
This chapter mainly concentrates on data-model fusion RUL prediction methods of mechanical systems. In industries, it is generally feasible to obtain both prior mechanical system degradation knowledge and condition monitoring data, and the effective fusion of them would output more satisfactory RUL prediction results. Motivated by this superiority,...
Chapter
In this chapter, the data-driven RUL prediction methods for mechanical systems are presented. Since the deep learning algorithm has shown remarkbale advantages on prognosis problems in the current literature, the neural network-based methods are focused on in this chapter. First, the deep separable convolutional neural network-based RUL prediction...
Chapter
In this chapter, the conventional intelligent fault diagnosis methods for mechanical systems are presented. First, as one of the major AI technologies at present, the typical neural network models are briefly reviewed, as well as their applications in the fault diagnosis problems for mechanical systems. The radial basis function networks and the wa...
Chapter
This chapter presents intelligent fault diagnosis methods based on deep transfer learning, which largely enhance the fault diagnosis performance in the real-world applications. The deep learning architectures are expected to represent features automatically instead of feature extraction by human labor, and the transfer learning gives an approach to...
Chapter
In this chapter, hybrid intelligent fault diagnosis methods for mechanical systems are presented. Firstly, a multiple weighted K nearest neighbor (KNN) combination method is introduced, where the same input feature set is considered. Next, a multiple adaptive neuro-fuzzy inference systems combination approaches with different input feature sets is...
Article
Joint optimization of maintenance operations and spare parts inventory optimization is promising for ensuring the reliable and efficient operation of industrial systems. Extensive studies have been conducted on joint optimization emphasizing various aspects. However, those studies still suffer two limitations. 1) The considered system structures ar...
Article
Deep transfer learning-based fault diagnosis has been developed to correct the data distribution shift, promoting a diagnosis knowledge transfer across related machines. However, there are two weaknesses: 1) The assumption that all the target domain data are unlabeled is strict for robust applications of deep trans-fer learning to diagnosis across...
Article
Remaining useful life (RUL) prediction and maintenance optimization are two critical and sequentially connected modules in the prognostics and health management of machines. Due to the advantages of obtaining more accurate RUL prediction results and the effectiveness of addressing replacement scheduling and spare parts provision dynamically, ensemb...
Article
Predictive maintenance is one of the most promising ways to reduce the operation and maintenance (O&M) costs of wind turbines (WTs). Remaining useful life (RUL) prediction is the basis for predictive maintenance decision. Self-data-driven methods predict the RUL of a WT driven by its own condition monitoring data without depending on failure event...
Article
It has always been an issue of significance to diagnose compound faults of machines. Existing intelligent diagnosis methods have to be trained by sufficient data of each compound fault. However, both labeled and unlabeled data of mechanical compound faults are usually difficult to collect or even completely inaccessible for training in real scenari...
Article
Most of the current successes of deep transfer learning-based fault diagnosis require two assumptions: 1) the health state set of source machines should overlap that of target machines; 2) the number of target machine samples is balanced across health states. However, such assumptions are unrealistic in engineering scenarios, where target machines...
Article
In recent years, intelligent data-driven prognostic methods have been successfully developed, and good machinery health assessment performance has been achieved through explorations of data from multiple sensors. However, existing datafusion prognostic approaches generally rely on the data availability of all sensors, and are vulnerable to potentia...
Article
Degradation modeling aims to formulate the health state degradation process of machinery. Commonly used degradation models pay more attention to describing the global increasing or decreasing trend without considering the local fluctuation in the degradation process. To deal with the above issue, this paper proposes a multi-model fusion degradation...
Article
The successful applications of deep transfer learning to intelligent fault diagnosis testify to a positive correlation between transferable feature similarity and knowledge transferability across diagnostic tasks. This correlation makes feature similarity possible to assess diagnostic knowledge transferability. Therefore, researchers have attempted...
Article
Applications of deep transfer learning to intelligent fault diagnosis of machines commonly assume symmetry among domains: 1) the samples from target machines are balanced across all health states, and 2) the diagnostic knowledge required by target machines is consistent with source machines. In reality, however, such assumptions cannot be justified...
Article
Remaining useful life (RUL) prediction is critical for ensuring the safe and efficient operation of machinery. Due to the existence of multiple influencing factors, the degradation of machinery is often described as dependent competing failure processes (DCFPs). Extensive studies have been conducted on the degradation modeling and RUL prediction fo...
Article
Deep learning (DL) based diagnosis models have to be trained by large quantities of monitoring data of machines. However, in real-case scenarios, machines operate under the normal condition in most of their life time while faults seldom happen. Therefore, though massive data are accessible, most are data of the normal condition while fault data are...
Article
Recently, deep learning is widely used in the field of remaining useful life (RUL) prediction. Among various deep learning technologies, recurrent neural network (RNN) and its variant, e.g., long short-term memory (LSTM) network, are gaining more attention because of their capability of capturing temporal dependence. Although the existing RNN-based...
Article
Semi-observable systems are referred to as a kind of widely used industrial equipment whose physical degradation state is only observable via shutdown inspection. To monitor the degradation process of semi-observable systems online, different types of sensors are generally employed to collect monitoring signals. Lots of studies have been conducted...
Article
Deep transfer-learning-based diagnosis models are promising to apply diagnosis knowledge across related machines, but from which the collected data follow different distribution. To reduce the distribution discrepancy, Gaussian kernel induced maximum mean discrepancy (GK-MMD) is a widely used distance metric to impose constraints on the training of...
Article
With the rapid development of Industrial Internet of Things, more and more sensors have been used for condition monitoring and prognostics of industrial systems. Big data collected from sensor networks bring abundant information resources as well as technical challenges for remaining useful life (RUL) prediction. The major technical challenges incl...
Article
To integrate the complete degradation information of machinery, deep learning-based prognostics approaches usually use monitoring data acquired by different sensors as the inputs of networks. These approaches, however, lack an explicit learning mechanism to effectively identify the distinctions of different sensor data and highlight the important d...
Article
Joint maintenance and spare parts inventory optimization has attracted increasing attention in recent years because of its capability in addressing the maintenance planning and the spare parts provisioning of industrial systems simultaneously. However, imperfect maintenance (IM) actions are either neglected or over-simplified as constant improvemen...
Article
As a deep learning model, deep belief network (DBN) consists of multiple restricted Boltzmann machines (RBMs). Based on DBN, many intelligent fault diagnosis methods are proposed. However, these methods seldom considered the appearance of new working conditions during the operation of real machines. Varying working conditions lead to a change of fe...
Article
Intelligent fault diagnosis (IFD) refers to applications of machine learning theories to machine fault diagnosis. This is a promising way to release the contribution from human labor and automatically recognize the health states of machines, thus it has attracted much attention in the last two or three decades. Although IFD has achieved a considera...
Conference Paper
Deep learning is gaining growing interests in the field of remaining useful life (RUL) prediction and has achieved state-of-the-art results. Current deep learning-based prognostics approaches, however, do not consider the distinctions of different sensor data during representation learning, which affects their prediction accuracy and limits their g...
Article
The presence of incorrect data leads to the decrease of condition-monitoring big data quality. As a result, unreliable or misleading results are probably obtained by analyzing these poor-quality data. To improve the data quality, an incorrect data detection method based on an improved local outlier factor (LOF) is proposed for data cleaning. First,...
Data
XJTU-SY bearing datasets can be downloaded by the following link: http://biaowang.tech/xjtu-sy-bearing-datasets. If you have any questions or suggestions, do not hesitate to contact: wangbiaoxjtu@outlook.com
Article
The growth of the Industrial Internet of Things (IIoT) has generated a renewed emphasis on research of prognostic degradation modeling whereby degradation signals, such as vibration signals, temperature and acoustic emissions, are used to estimate the state-of-health and predict the remaining useful life (RUL). Besides the inherent system state, ex...
Article
Remaining useful life (RUL) prediction of rolling element bearings plays a pivotal role in reducing costly unplanned maintenance and increasing the reliability, availability, and safety of machines. This paper proposes a hybrid prognostics approach for RUL prediction of rolling element bearings. First, degradation data of bearings are sparsely repr...
Article
The search for new methods of determining the degree of stress of steel structures and the methods of detecting early phases of defect development are still proceeding. These allows to enhance the safety of use of machines and construction structures. The paper presents the description of a measuring apparatus, the method of measurements and the re...
Article
Recently, bearing health condition monitoring has attracted considerable attention due to its great significance to prolong the lifespan and improve the system reliability of key industrial equipments. For the purpose of improving the reliability and effectiveness of energy harvesting in the monitoring node of industrial equipments, this paper prop...
Article
Deep learning has attracted attentions in intelligent fault diagnosis of machinery because it allows a deep network to accomplish the tasks of feature learning and fault classification automatically. Among deep learning models, convolutional neural networks (CNNs) are able to learn features from mechanical vibration signals and thus several studies...
Conference Paper
It is difficult to train a reliable intelligent fault diagnosis model for machines used in real cases (MURC) because there are not sufficient labeled data. However, we can easily simulate various faults in a laboratory, and the data from machines used in the laboratory (MUL) contain fault knowledge related to the data from MURC. Thus, it is possibl...
Conference Paper
Full-text available
Remaining useful life (RUL) prediction of machinery is a major task in condition-based maintenance, which is able to provide crucial guidance for preventive maintenance. To guarantee the accuracy for the RUL prediction of machinery subjected to two-phase degradation process, the interactive multiple model (IMM) filtering technique has been used bec...
Article
Remaining useful life (RUL) prediction has attracted more and more attention in recent years because of its significance in predictive maintenance. The degradation processes of systems from the same population are generally different from each other due to their various operational conditions and health states. This behavior is defined as unit-to-u...
Article
Machinery prognostics is one of the major tasks in condition based maintenance (CBM), which aims to predict the remaining useful life (RUL) of machinery based on condition information. A machinery prognostic program generally consists of four technical processes , i.e., data acquisition, health indicator (HI) construction, health stage (HS) divisio...
Article
The presence of repetitive transients in vibration signals is a typical symptom of local faults of rotating machinery. Infogram was developed to extract the repetitive transients from vibration signals based on Shannon entropy. Unfortunately, the Shannon entropy is maximized for random processes and unable to quantify the repetitive transients buri...
Article
In the study of data-driven prognostic methods of machinery, much attention has been paid to constructing health indicators (HIs). Most of the existing HIs, however, are manually constructed for a specific degradation process and need the prior knowledge of experts. Additionally, for the existing HIs, there are usually some outlier regions deviatin...
Article
Faults are a potential killer of large-scale mechanical equipment, such as wind power equipment, aircraft engines and high-end CNC machine. And fault diagnosis plays an irreplaceable role in ensuring the health operation of such equipment. Since the amount of the equipment diagnosed is great and the number of the sensors for the equipment is large,...
Article
Degradation-based modelling methods have been recognized as an essential and effective approach for lifetime and remaining useful life (RUL) estimations for various health management activities that can be scheduled to ensure reliable, safe, and economical operation of deteriorating systems. As one of the most popular stochastic modelling methods,...
Article
Full-text available
In modern rotating machinery, rotary encoders have been widely used for the purpose of positioning and dynamic control. The study in this paper indicates that, the encoder signal, after proper processing, can be also effectively used for the health monitoring of rotating machines. In this work, a Kurtosis-guided local polynomial differentiator (KLP...
Chapter
This chapter focuses on the remaining useful life (RUL) prediction of rotating machinery. It first gives an explicit description to the concept of RUL prediction and illustrates the major processes of the RUL prediction. Then, two categories of RUL prediction methods, that is, data-driven methods and model-based methods are discussed in details, re...
Chapter
This chapter gives an overall introduction and background of this book. It starts by introducing the importance of rotating machinery in industrial applications. Then, some commonly used components of rotating machinery are briefly introduced. From the analysis of some reports it is concluded that, the health management of rotating machinery is sig...
Chapter
This chapter mainly focuses on signal processing and feature extraction. In order to achieve intelligent fault diagnosis and remaining useful life prediction, it is essential to process the collected vibration signals from machinery and extract fault features by using signal processing techniques and methods, such as wavelet transform, Hilbert–Huan...
Chapter
Full-text available
This chapter mainly focuses on hybrid intelligent fault diagnosis methods with multiple classifier combination. Since the multiple classifier combination with different, either input feature sets or classification algorithms, usually exhibits complementary classification behavior, this chapter investigates the classification accuracy of multiple cl...
Chapter
This chapter introduces the intelligent diagnosis methods based on individual intelligent techniques. The concept and advantages of intelligent diagnosis are first described, as well as the main steps commonly included in intelligent diagnosis. Second, three methods using artificial neural networks, which are able to learn and generalize nonlinear...
Chapter
Full-text available
Clustering algorithms can automatically recognize the pattern inside the data so as to analyze the collected data without their labels. Using this advantage, three clustering-based fault diagnosis methods are presented to deal with some diagnosis cases of rotating machinery in which the labeled data are limited. In the first method, compensation di...
Article
Time-varying gearmesh stiffness (TVGS) is the main cause of gear vibration, and its accuracy affects the responses of dynamic models. An exponential curve model based on the Saint Venant’s Principle is proposed to calculate the gearmesh stiffness of cracked spur gears in this paper. With the proposed model, the TVGS under the circumstances of healt...
Article
Most traditional overdamped monostable, bistable and even tristable stochastic resonance (SR) methods have three shortcomings in weak characteristic extraction: (1) their potential structures characterized by single stable-state type are insufficient to match with the complicated and diverse mechanical vibration signals; (2) they vulnerably suffer...
Article
Full-text available
Varying speed conditions bring a huge challenge to incipient fault detection of rolling element bearings because both the change of speed and faults could lead to the amplitude fluctuation of vibration signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient fault dete...
Conference Paper
Full-text available
The remaining useful life (RUL) prediction of bearings has emerged as a critical technique for providing failure warnings in advance, reducing costly unscheduled maintenance and enhancing the reliability of bearings. Recently, a fusion prognostics method combining exponential model and relevance vector machine (RVM) has been proposed and applied to...
Conference Paper
Traditional overdamped stochastic resonance (SR) methods are difficult to match with complicated and variable input signals due to single stable-state types. Moreover, their performance depends on the parameter selection of highpass filters. To further explore the potential of SR, this paper studies the behavior of underdamped SR in a multistable n...
Chapter
Rotating machinery covers a broad range of mechanical equipment in industrial applications. It generally operates under tough working environment and is therefore subject to faults easily. Vibration signals collected in the working process have valuable contributions for the presentation of conditions of the rotating machinery. Consequently, using...
Article
In remaining useful life (RUL) prediction, stochastic process models are widely used to describe the degradation processes of systems. For age-dependent stochastic process models, the RUL probability density function (PDF) can be calculated using a closed-form solution. For state-dependent models, however, it is difficult to calculate such a closed...
Article
In mechanical fault diagnosis, most traditional methods for signal processing attempt to suppress or cancel noise imbedded in vibration signals for extracting weak fault characteristics, whereas stochastic resonance (SR), as a potential tool for signal processing, is able to utilize the noise to enhance fault characteristics. The classical bistable...
Article
In data-driven prognostic methods, prediction accuracy of bearing remaining useful life (RUL) mainly depends on the performance of bearing health indicators, which are usually fused from some statistical features extracted from vibration signals. However, many existing bearing health indicators have the following two shortcomings: (1) many statisti...
Article
The influence of potential asymmetries on stochastic resonance (SR) subject to both multiplicative and additive noise is studied by using two-state theory, where three types of asymmetries are introduced in double-well potential by varying the depth, the width, and both the depth and the width of the left well alone. The characteristics of SR in th...
Article
Remaining useful life (RUL) prediction is a key process in condition-based maintenance for machines. It contributes to reducing risks and maintenance costs and increasing the maintainability, availability, reliability, and productivity of machines. This paper proposes a new method based on stochastic process models for machine RUL prediction. First...
Conference Paper
Full-text available
This paper proposes a machine remaining useful life (RUL) prediction method based on Monte Carlo simulation. In this method, the degradation processes of machinery are described using a general stochastic process model, which assumes the degradation speed is dependent on the age and the health state. The machine RUL is predicted using a numerical a...
Article
Because planetary gear sets contain many components and have complex transmission structures, their dynamic models are difficult to be established. Besides, the planet gears rotate around not only their own centers, but also the center of the sun gear, and the time-varying distances from gear meshing points to the fixed transducers add the complexi...
Article
Full-text available
Intelligent fault diagnosis is a promising tool to deal with mechanical big data due to its ability in rapidly and efficiently processing collected signals and providing accurate diagnosis results. In traditional intelligent diagnosis methods, however, the features are manually extracted depending on prior knowledge and diagnostic expertise. Such p...
Article
Rolling element bearings are one of the fundamental and most important elements in machines and their failures are among the foremost frequent causes of machine breakdown. Vibration and acoustic signals from faulty bearings are typically a mixture of fault-induced periodic impulses and other components. Traditional time-domain features like root-me...
Conference Paper
In the studies of intelligent fault diagnosis of machines, lots of effort goes into designing effective feature extraction algorithms. Such processes would consume plenty of human labor, especially when dealing with massive vibration signals. So it is interesting to automatically extract features using machine learning techniques, instead of manual...
Article
In view of some shortcomings of support vector machine, for instance, it is difficult to select the regularization parameter and the kernel function must satisfy Mercer's condition, relevance vector machine (RVM) is developed and applied to the field of trend prediction. The performance of RVM, to a large extent, depends on the kernel function. How...