Naipeng Li

Naipeng Li
Xi'an Jiaotong University | XJTU · Department of Mechatronics Engineering

Doctor of Engineering

About

59
Publications
57,366
Reads
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6,103
Citations
Citations since 2016
51 Research Items
6013 Citations
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201620172018201920202021202205001,0001,5002,000
201620172018201920202021202205001,0001,5002,000
Additional affiliations
September 2012 - June 2020
Xi'an Jiaotong University
Position
  • Xi‘an

Publications

Publications (59)
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
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
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
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
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
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
With the increasing demand for stability, safety, and reliability of commissioned machines, diverse types of sensors are positioned on key components. To exploit these multisource data, more and more deep learning-based remaining useful life (RUL) prediction approaches are developed recently. These approaches, however, still suffer from the followi...
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
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
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
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
Due to the data distribution discrepancy, fault diagnosis models, trained with labeled data in one scene, likely fails in classifying by unlabeled data acquired from the other scenes. Transfer learning is capable to generalize successful application trained in one scene to the fault diagnosis in the other scenes. However, the existing transfer meth...
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
In the industrial process, the safety and reliability of the mechanical system determine the quality of the product, and whether small faults can be diagnosed in time is the key to ensuring the safe operation of the system and restraining the deterioration of faults. In recent years, the data-driven fault diagnosis has attracted widespread attentio...
Article
Full-text available
Variational mode decomposition provides a feasible method for non-stationary signal analysis, but the method is still not adaptive, which greatly limits the wide application of the method. Therefore, an adaptive spectrum mode extraction method is proposed in this article. The proposed method is mainly composed of spectral segmentation, mode extract...
Article
Ensemble learning aggregates the predictions of diverse predictors. Most existing ensemble learning methods gather the base classifiers based on the weight of accuracy. However, if the weight is adjusted only according to the correct rate of the base classifiers, it ignores the different types of errors caused by different working principles of the...
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
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
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
Full-text available
Prognostics and health management (PHM) is crucial for ensuring the safe operation of machinery, improving the productivity and increasing economic benefits. High-quality life-cycle data, as the basic resource in the field of PHM, are able to carry the key information which reflects the complete degradation processes of machinery. However, due to t...
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...
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
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
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
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...
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
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
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...
Chapter
Planetary gearboxes are widely used in modern industry because of their advantages of large transmission ratio, strong load-bearing capacity, etc. Planetary gearboxes differ from fixed-axis gearboxes and exhibit unique behaviors, which increase the difficulty of fault detection. The vibration based signal processing technique is one of the principa...
Conference Paper
Full-text available
This paper proposes a nonlinear degradation model based method for remaining useful life (RUL) prediction of rolling element bearings. First, a new nonlinear degradation model is constructed which considers four variable sources of stochastic degradation processes of bearings simultaneously, i.e., the temporal variability, the unit-to-unit variabil...
Article
The remaining useful life (RUL) prediction of rolling element bearings has attracted substantial attention recently due to its importance for the bearing health management. The exponential model is one of the most widely used methods for RUL prediction of rolling element bearings. However, two shortcomings exist in the exponential model: (1) the fi...
Conference Paper
Full-text available
Rolling element bearings are one of the most widely used components in rotating machinery. However, they are also the components which frequently suffer from damage. Remaining useful life (RUL) prediction of rolling element bearings has received considerable attention, since it can avoid failure risks, and ensure availability, reliability and secur...
Article
Full-text available
The vibration based signal processing technique is one of the principal tools for diagnosing faults of rotating machinery. Empirical mode decomposition (EMD), as a time-frequency analysis technique, has been widely used to process vibration signals of rotating machinery. But it has the shortcoming of mode mixing in decomposing signals. To overcome...
Article
Planetary gearboxes are widely used in aerospace, automotive and heavy industrial applications that require compactness and high torque-to-weight ratios. Despite these advantages, tough operation conditions under which planetary gearboxes are typically used may lead to damage on their key components, for example, gears and bearings. Because of the...

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Projects (2)
Project
Predict the remaining useful life of rotating machinery using model-based or data-driven methods