Jing Lin

• PhD
• Professor at Beihang University

Wire-arc additive manufacturing (WAAM) is acknowledged as a highly effective and economical 3D printing method for large component production and repair. However, defects like porosity, oxidization, and distortion may occur due to the high deposition rate and improper manufacturing process, leading to a vital concern about the structural quality. A...

Aero engine is the core component of the whole aircraft, and the blade plays a significant role in the aero engine. The working environment of the blade is often very harsh, so the probability of damage is high. Due to the variable cross-section characteristics of blade structure, traditional Lamb wave damage detection methods cannot be directly ap...

Bolt joints are commonly used in aviation structures. Bolt looseness may pose serious safety risks and its online monitoring is of great importance to structure and air safety. Active guided wave detection methods can accurately identify the tightness status of bolts. However, the excitation of active guided waves requires big and heavy equipment,...

Despite the impressive process of current domain adaptation-based fault diagnosis approaches, access to source data and source model parameters is a sine qua non, resulting in obvious limitations when deploying to real industry, particularly considering the data storage, transmission, and privacy issues. In light of this, an interesting and challen...

The acoustic emission (AE) technique serves as a robust alternative in machine condition monitoring, demonstrating particular sensitivity to incipient failures. Traditional cyclostationary analysis relies on prior knowledge of repetitive transients, limiting its use when the system’s dynamic model is unknown. A cyclostationary method is also urgent...

The structural integrity and safety of carbon fiber reinforced plastics (CFRP) are vulnerable to delamination, which is often imperceptible to the naked eye. Although the Scanning Laser Doppler Vibrometer (SLDV) has shown promise in damage quantification of CFRP, its timeconsuming measurement process limits its application in engineering scenarios....

Decomposition methods which can separate the fault components into different modes have been widely applied in bearing fault diagnosis. However, early fault diagnosis is always a challenge for the signal processing methods as well as the traditional decomposition methods due to the heavy noise. Therefore, how to extract the weak fault information f...

Despite the remarkable success of end-to-end intelligent diagnosis methods, the shortage of available training data remains one of the most challenging issues in real industrial scenarios. In light of this, a wide variety of deep generative models are developed for data volume expansion. Notably, the denoising diffusion probabilistic model (DDPM) h...

The presence of corrosion damage in metallic structures is a critical problem affecting system safety, which poses challenges for structural maintenance. Ultrasonic Lamb wave testing has shown many benefits, including high sensitivity and a large coverage area. However, multiple external factors are causing a mismatch between the imaging result and...

Recent progress in digital twin (DT) has significantly contributed to the advancement of predictive maintenance. The interaction of data between physical and virtual models is facilitated through carefully designed health indicators (HIs). Conventional condition monitoring HIs are inadequate for early-stage fault detection and lack the capacity to...

Fault detection can promptly reveal the potential hazards of mechanical equipment, guaranteeing the safety, stability, and reliability of their operation. Although many advanced fault detection methods, such as spectral kurtosis, deconvolution and decomposition, have been developed, most of them still suffer from insufficient utilization of fault f...

Motor current signature analysis (MCSA), as a non-invasive diagnostic method, is robust to environmental noise and of less sensor cost than vibration-based monitoring. Although large amount of progress has been achieved, little attention is paid to the MCSA-based diagnosis task in the Servo motion systems (SMS). An approach using Park vector demodu...

The ultrasonic Lamb wave testing (ULWT) has proven valuable in non-destructive testing (NDT) due to its high sensitivity and wide coverage. However, the classical post-processing algorithm, the delay-and-sum (DAS) technique, suffers from notable artifacts and inadequate accuracy in Lamb wave inspection due to the existence of reflection and superpo...

Cyclic spectral coherence (CSCoh) is an effective tool to reveal the cyclostationarity of the fault-induced components (FICs). Integrating CSCoh over the domain of the spectral frequency or decomposing CSCoh by matrix factorization methods can provide an enhanced diagnosis spectrum. However, for compound faults, the integration-based strategy or th...

The acoustic emission (AE) technique is known for its sensitivity to early damage and is well-suited for online condition monitoring of rolling element bearings (REBs) in various industrial application scenarios. Nonetheless, identifying weak faults under varying speed and strong background noise conditions still remains challenging. In addition, t...

Fault detection of planetary gearboxes is of vital importance for the operation safety of large equipment. Built-in information, including the encoder signal which can detect the slight torsional vibration caused by the machinery fault, has an outstanding superiority for the fault feature extraction. Yet, the differentiate operation of the encoder...

Owing to recent studies on the relationship between the transmission error and the localized gear fault, TE-based differential diagnosis (DD) technology has emerged as a promising approach to identifying the tooth flank spall and the gear fillet crack which have similar signatures but quite different fault mechanisms and prognoses. However, due to...

Flow-induced random vibration propagates throughout a plate-like structure and thus contains information about the structural properties and states. Previous research has shown that the group velocity under various frequencies can be estimated by the cross-correlation between two measurement points, in which the prominent peak signifies the travel...

Knee diseases such as osteoarthritis and patellofemoral disorder can cause abnormal patellar motion and lead to the reduction of a person’s activity ability. In this work, a new kind of flexible sensors combining graphene particles, lead magnesium niobate-lead titanate (PMN-PT) and polyvinylidene fluoride (PVDF) to form a flexible ternary composite...

Crack damage is one of the significant factors that may accumulate at the stress concentration area of engineering structures and cause catastrophic accidents. In this paper, we proposed a novel approach to identify the crack location and size by exploiting the reflections and diffractions of Lamb waves. The interaction mechanism between the crack...

Deconvolution methods have been widely used in machinery fault diagnosis. However, their application would be confined due to the heavy noise and complex interference since the fault feature in the measured signal becomes rather weak. Time synchronous averaging (TSA) can enhance the periodic components and suppress the others by the comb filter fun...

Due to the severe working condition and long-term service, the key rotating parts including the bearing and gearbox, are susceptible to damage. Blind deconvolution which can eliminate the influence of the transfer path and enhance the fault-related feature is widely used for machinery fault diagnosis. Not only the objective function but the initial...

Data-driven deep learning approaches have been recently developed for guided wave-based structural health monitoring. However, the difficulty in collecting and labeling valid samples often leads to a small dataset in practical damage identification, lowering the performance of the trained model. In addition, conventional deep learning models often...

In this study, graphene particles are introduced to the lead magnesium niobate-lead titanate and polyvinylidene fluoride (PVDF) to form a flexible ternary composite. The graphene concentration is rigorously designed and morphologically optimized, warranting good piezoelectric and dielectric properties. The piezoelectric and dielectric performances...

The Scanning Laser Doppler Vibrometer (SLDV) has emerged as a powerful tool for acquiring full wavefield data of propagating Lamb waves in carbon fiber reinforced plastics (CFRP). Conventional full wavefield scanning techniques, however, suffer from being time and energy-intensive, as they require a sufficient number of sampling points to maintain...

The process health monitoring of wire arc additive manufacturing (WAAM) is significant for product quality. Most existing additive manufacturing process monitoring is based on image data such as temperature and spatters. However, these monitoring methods do not reflect status information promptly. Moreover, the issue of limited cross-domain diagnos...

To cater to fault diagnosis of rotating machinery under complex working conditions, unsupervised domain adaptation technology has been widely explored and applied. Existing methods mainly reduce domain bias in two ways, including metric learning and discriminator-based adversarial learning. Different from these technologies, in this work, we only r...

For the modeling of Lamb wave propagation, the accuracy of some two-dimensional (2D) plate theories like the first order shear deformation theory (FSDT) may decline at a high frequency, since their displacement fields cannot match those complicated Lamb wave structures. To address this issue, this paper proposes an improved 2D thin plate theory whi...

Many nondestructive testing and structural health monitoring systems utilize Lamb wave transducer arrays for identification and localization of scattering wave sources. A large number of transducers are usually required during array signal processing and source localization, which increases hardware cost and reduces system reliability. To overcome...

Deep learning-based fault diagnosis methods have to be trained by a large amount of labeled data for accurate diagnosis results. However, in real-case industrial scenarios, the data available is limited since the difficulty of obtaining fault samples. In addition, the monitoring data always come from various working conditions. As a result, data sc...

Adhesive lap joints between composite and metal plates have been widely used in industrial fields including the automotive industry, marine manufacturing and aerospace engineering. Low quality of operation, harsh environment, adhesive aging and other disadvantages may lead to disbonding. To assess the disbond contour at an adhesive interface, this...

This paper proposed an Acoustic Emission (AE) based Smart Composite Fastener (SCF) concept for health monitoring of bonded/bolted composite single lap joints. The SCF was made of 3D-printed continuous carbon fibre reinforced thermoplastic materials with an embedded piezoelectric sensor. The SCF detected signals were found to be successfully associa...

Composite materials are progressively employed in many safety-critical structural applications due to their superior properties. Structural health monitoring techniques based on Lamb waves have been utilized to assess the damages of composite structures. Recently, deep learning algorithms are adopted for damage detection and localization. Identifyi...

Adhesively bonded joints between metallic plates and composite laminates are frequently used in various fields including aviation, automobile industry, and marine manufacturing. The edge of adhesive layer is exposed to pollutants in the air, which implies that the joints are inclined to suffer debonding defects along the adhesive bond line. To eval...

Structural health monitoring of long-span bridge has received increasing attention in recent years. In order to achieve accurate monitoring, the integrity of data collection should be guaranteed. Unfortunately, these data inevitably contain a variety of types of anomalies due to sensor faults, harsh environments, and other issues. Identifying anoma...

Existing quantification models using Lamb waves are generally data-driven models, and the model choice can have a significant impact on the quantification results and the probability of detection (POD). This study develops a general method of model averaging and probability of detection estimation for Lamb wave detection. By treating each of the da...

The vibration signals of variable speed rotating machines are non-stationary. Time-frequency analysis (TFA) can effectively analyze non-stationary signals in time-frequency (TF) plane and polynomial chirplet transform (PCT) is one of widely adopted TFA methods. In PCT, a vital step is to approximate the instantaneous frequency (IF) of signals throu...

Rolling bearing is an indispensable part in rotary machinery, and its fault diagnosis and life prediction have been a hot issue in the field of research and engineering. With the development of fault diagnosis technology, researchers pay a growing attention to skidding which is easy to cause incipient bearing failure. Due to the complex structure a...

In this article, a new decomposition theory, feature mode decomposition (FMD), is tailored to the feature extraction of machinery fault. The proposed FMD is essentially for the purpose of decomposing the different modes by the designed adaptive finite impulse response (FIR) filters. Benefitted from the superiority of correlated kurtosis, FMD takes...

To alleviate the predicament of data annotating and the need for collecting data from identical distribution, unsupervised domain adaptation technologies have been widely deployed in the field of machine fault diagnosis. Nevertheless, most of them focus only on domain alignment and fail to make full use of the unlabeled target data. Given this, a n...

Domain adaptation technology has been intensively studied in machine fault diagnosis for more reliable diagnosis performance. Nonetheless, most approaches rely on the availability of source data, which is always unattainable in many practical industrial scenarios due to the costs of expensive data storage and transmission, as well as privacy protec...

Lamb wave minimum variance imaging is a promising method for visual damage identification and localization with a sparse transducer array. Imaging performance of minimum variance is highly dependent on the design accuracy of look-direction to describe amplitude relationship of array reflection signals. Look-direction is the combination of a directi...

Driven by industrial big data and intelligent manufacturing, deep learning approaches have flourished and yielded impressive achievements in the community of machine fault diagnosis. Nevertheless, current diagnosis models trained on a specific dataset seldom work well on other datasets due to the domain discrepancy. Recently, adversarial domain ada...

Lamb wave-based damage identification and localization methods hold the potential for nondestructive evaluation and structural health monitoring. Dispersive and multimodal characteristics lead to complicated Lamb wave signals that are challenging to be analyzed. Deep learning architectures could identify damage-related features effectively. Convolu...

This paper proposes an improved double-dictionary K-singular value decomposition (IDDK-SVD) algorithm for the compound fault diagnosis of rolling element bearings under complex industrial environments. In the framework, a double-dictionary is first designed for respectively identifying and distinguishing compound-fault features. In addition, an ato...

A thorough understanding of the scattering mechanism of Lamb waves at discontinuities is of interest for quantitative evaluation of structural properties and mode control. This study extends the generalized scattering matrix method to investigate the interaction of straight crested Lamb waves with multiple cascaded rectangular notches. Based on the...

Orthotropic steel bridge decks and steel box girders are key structures of long-span bridges. Fatigue cracks often occur in these structures due to coupled factors of initial material flaws and dynamic vehicle loads, which drives the need for automating crack identification for bridge condition monitoring. With the use of unmanned aerial vehicle (U...

The scattered fields of Lamb waves resulting from damage have been extensively studied because they carry rich structural information that allow us to identify and evaluate damage. This paper investigates the interaction of the fundamental symmetric Lamb mode (S0) near the first Lamé point with opening cracks of various heights under the plane stra...

Signature extraction of fault impacts is a significant task for rolling bearing diagnosis. A series of blind source and target deconvolution methods respectively represented by minimum entropy deconvolution (MED) and maximum correlated kurtosis deconvolution (MCKD) are proposed to enhance the fault impulses from the complex interference components....

Fault diagnosis is of significance for ensuring the safe and reliable operation of machinery equipment. Due to the heavy noise and interference, it is difficult to detect the fault directly from the measured signal. Hence, signal processing techniques that can achieve feature extraction, signal denoising, and fault identification are the most commo...

To meet industrial demand, plenty of research works have been dedicated to monitoring the health status of planetary gearboxes. For the same purpose, a new path is explored in this work based on rotating encoder signal (RES) by considering arbitrary speed condition. In this framework, generalized demodulated (GD) is applied on second-order derivati...

The core of structural health monitoring is to provide a real-time monitoring, inspection, and damage detection of structures. As one of the most promising technology to structural health monitoring, the Lamb wave method has attracted interest because it is sensitive to small-scale damage with a long detection range. However, in many real-world str...

The key idea behind demodulation analysis for bearing diagnosis is to determine the fault-induced frequency band and directly detect the potential bearing fault characteristic frequency (FCF) in the demodulated spectrum. Till now, most demodulation methods are based on the optimal selection of only one informative frequency band. However, the unwan...

To realize fault identification of unlabeled data and improve model generalization capability, domain adaptation has been increasingly applied to intelligent fault diagnosis of machinery. Nevertheless, traditional domain adaptation diagnosis models generally constrain different domains to have the same label space, which is not always hold in compl...

Behind the brilliance of the deep diagnosis models, the issue of distribution discrepancy between source training data and target test data is being gradually concerned for catering to more practical and urgent diagnostic requirements. Consequently, advanced domain adaptation algorithms have been introduced to the field of fault diagnosis to addres...

Maximum second-order cyclostationarity blind deconvolution (CYCBD) outperforms other deconvolution methods in retrieving the weak periodic impulses related to bearing incipient faults. However, the main challenge in the practical application of CYCBD is how to set several key parameters appropriately, the uppermost of which is the targeted cyclic f...

Lamb wave spectral methods are one candidate to characterize invisible damages in composite structures. Unfortunately, multiple reflections resulting from geometric boundaries could distort the Lamb wave spectrum, which may cover the important signatures concerning structural integrity. To eliminate spectral interference, a cepstrum based filtering...

Ultrasonic phased array (UPA) provides a powerful tool for nondestructive testing (NDT) of carbon fiber-reinforced plastic (CFRP). By the aid of full matrix capture (FMC) technique, the optimum resolution of anisotropic CFRP inspection could be achieved by the total focusing method (TFM). The directional dependence of ultrasonic velocity is one of...

Blind deconvolution (BD) is a popular tool for vibration analysis, which has been extensively studied to extract useful information from contaminative signals for the diagnosis of rotating machinery. However, due to the disturbance of diverse interferences, good performance of conventional BD methods is usually hard to be guaranteed in some situati...

Owing to carrying rich information about structure flaws, broadband Lamb waves are considered as a promising tool for non-destructive testing. However, since every Lamb wave mode has its own dispersion characteristics, the feature extraction among broadband multimodal Lamb wave is challenging. Time–frequency representation is significantly effectiv...

Deconvolution methods have been proven to be effective tools to extract excitation sources from the noisy measured signal. However, its application is confined by the extraction of incomplete information. To tackle this problem, a new deconvolution method, named period-oriented multi-hierarchy deconvolution (POMHD) is proposed in this paper. Variou...

Domain adaptation technologies have been extensively explored and successfully applied to machine fault diagnosis, aiming to address problems that target data are unlabeled and have a certain distribution bias with source data. Nonetheless, existing fault diagnosis methods mainly explore feature-level alignment strategies to reduce domain discrepan...

The comprehensive assessment of crack angle, length and profile could provide an important reference for the non-destructive evaluation of plate-like structures. In this paper, we aim at fully exploiting information carried by reflections, so that details of the crack could be displayed as much as possible. Firstly, the Lamb wave reflections from a...

Although deep networks based diagnostic methods have been increasingly studied and acquired certain achievements in recent years, most of them suppose that the training and test data share similar probability distribution. The data distribution discrepancy is common and inevitable in practical industry due to the change of working conditions, equip...

Trailing pulses, which are Lamb waves generated at large frequency-thickness (fd) products, act like a list of longitudinal pulses with constant time interval. These longitudinal pulses with short wave length are a promising tool for detection of damage in thick plates. However, the waveform complexity of trailing pulses brings challenges. In this...

Sparse fault transient extraction is the primary step in rotating machine fault detection. In the present paper, periodical convolutional sparse representation (PCSR) is proposed for reliable separation of fault transients imbedded in raw vibration signals. Specifically, a sparse optimization problem of PCSR is constructed, in which periodical faul...

Full wavefield processing techniques are promising for damage detection and imaging. However, the high measuring requirements in space are labor-consuming. To improve the imaging quality of wavefields with limited scanning points, 2D interpolation methods are investigated with simulating wavefields. To detect the defects, a damage index called firs...

The paper presents a quantitative Lamb wave detection method for delamination characterization in composite laminates using local wavenumber features. In contrast to the conventional Fourier transform based methods, the improved sparse reconstruction method is efficient and able to evaluate the spatial wavenumbers of Lamb waves with limited measure...

At present the majority of the research works on machining chatter detection are based on energy level monitoring of certain chatter frequencies which are premised to be invariant. However, chatter frequencies are very complicated with multi-frequency/frequency band, time-variant characteristics and are also affected by many factors in the machinin...

With the rapid development of manufacturing industry, machine fault diagnosis has become increasingly significant to ensure safe equipment operation and production. Consequently, multifarious approaches have been explored and developed in the past years, of which intelligent algorithms develop particularly rapidly. Convolutional neural network (CNN...