Vincent Suen

• PhD
• A.Prof. at Wenzhou University

Most of the traditional radio frequency identification (RFID) multi-tag realizes identification by using electromagnetic communication. However, with the increasing number of RFID multi-tags and the increasing complexity of electromagnetic interference in RFID application environments, only using traditional electromagnetic approach to realize RFID...

In recent years, tool condition monitoring (TCM) based on deep learning has been widely considered and achieved remarkable success. However, these methods typically require relatively large training samples to produce significant results, which are both imbalanced and rather troublesome to obtain in the practical application of TCM. To address this...

Rapid tool wear conditions during the manufacturing process are crucial for the enhancement of product quality. As an extension of our recent works, in this research, a generic in-situ tool wear condition monitoring during the end milling process based on dynamic mode and abnormal evaluation is proposed. With the engagement of dynamic mode decompos...

Surface roughness is of great significance in maintaining mechanical performance and improving the reliability of the equipment. However, fast surface roughness evaluations that are sufficiently stable and efficient for engineering in situ use have not yet been realized. To address this issue, an image-driven roughness intelligent method is propose...

Existing deep learning-based infrared image analysis methods have been widely utilized in gearbox fault. However, these methods still face problems such as insufficient amount of effective data and poor adaptability under variable working conditions. In this paper, a deep prototype network model combined with multi-scale module and coordinate atten...

With the increasing application numbers of RFID multitags in recent years, the position distribution of RFID multitag has a significant impact on the reading performance of RFID system. The poor reading performance of RFID multitag will lead to the loss of important information stored in the tags. In this paper, a novel synergistic topology graph c...

Health monitoring (HM) in rotatory machinery is a process of developing a mechanism to determine its state of deterioration. It involves analyzing the presence of damage, locating the fault, determining the severity of the problem, and calculating the amount of time that the machine can still be used effectively by making use of signal processing m...

The remaining useful life prediction of circuit breaker operating mechanisms is crucial for the condition-based maintenance of national power grids. To realize accurate remaining useful life prediction, a novel wavelet-enhanced dual-tree residual network is proposed in this paper. Through this wavelet transform, the time series is decomposed into t...

Accurate tool condition monitoring (TCM) is important for the development and upgrading of the manufacturing industry. Recently, machine-learning (ML) models have been widely used in the field of TCM with many favorable results. Nevertheless, in the actual industrial scenario, only a few samples are available for model training due to the cost of e...

Nonlinear and time-varying characteristics of mechanical systems may lead to the variation of fault modes which makes it hard to be accurately evaluated. To fulfill this requirement, the authors propose a time-domain signal-driven mechanical system state description method in this research. Based on the theory of stochastic subspace identification,...

Robust spring energy state identification of the operating mechanism is of great significance for monitoring the overall performance of the circuit breakers. However, rapid monitoring of the spring energy storage state based on the acquired current signal during the service period has not yet been realized. To address this problem, this research pu...

In recent years, deep learning- based methods have attracted much attention and achieved remarkable results for intelligent fault diagnosis of rotating machinery. However, in many actual industrial scenes, there are few labeled and large unlabeled samples that can be collected for learning due to the cost of experiments, which affect seriously the...

Accurate evaluation of the tool wear states during the machining process is significant for the enhancement of processing efficiency and product quality. To fulfill this requirement, as an extension of our recent works, the authors proposed an improved vision-based method to realize the segmentation and quantitative evaluation for tool wear monitor...

Uncertainty dimensions of geometrical features in film cooling holes will inevitably affect the aerothermal behavior and mechanical characteristics of the engine. To realize the measurement of key parameters of film cooling holes, this article introduces a vision-based method for dimensional in situ measurement of the holes in aero-engines during t...

Concrete slab cracks monitoring of modern high-speed railway is important for safety and reliability of train operation, to prevent catastrophic failure, and to reduce maintenance costs. This paper proposes a curvature filtering improved crack detection method in concrete slabs of high-speed railway via graph-based anomalies calculation. Firstly, l...

Tool condition monitoring (TCM) in numerical control machines plays an essential role in ensuring high manufacturing quality. The TCM process is conducted according to the data obtained from one or more of a variety of sensors, among which acoustic sensors offer numerous practical advantages. However, acoustic sensor data suffer from strong noise,...

Multisensor fusion technique is used to combine the complementary information source from the multiple sensors. However, the multisensor data are obviously different with the characteristics of complex types, different dimensions, or different weights, which is easy to cause the difficulty of the fusion and the decline of the ability of information...

Tool wear condition monitoring (TCM) is an important part of machining automation. In recent years, deep learning (DL) based TCM methods have been widely researched. However, almost DL-based methods need sufficient learning samples to obtain good accuracy, which is hard for TCM in terms of cost and time. In order to enhance the recognition accuracy...

The classification of electroencephalogram (EEG) signals is of significant importance in brain-computer interface (BCI) systems. Aiming to achieve intelligent classification of motor imagery EEG types with high accuracy, a classification methodology using the wavelet packet decomposition (WPD) and the proposed deep residual convolutional networks (...

Accurate identification of the type of seizure is very important for the treatment plan and drug prescription of epileptic patients. Artificial intelligence has shown considerable potential in the fields of automated EEG analysis and seizure classification. However, the highly personalized representation of epileptic seizures in EEG has led to many...

Tool wear condition monitoring (TCM) is essential for milling process to ensure the machining quality, and the long short-term memory network (LSTM) is a good choice for predicting tool wear value. However, the robustness of LSTM- based method is poor when cutting condition changes. A novel method based on data fusion enhanced LSTM is proposed to e...

The air-gap eccentricity will produce unbalanced magnetic pull and cause vibrations and noises in a motor. In this study, the dynamic behavior of a synchronous motorized spindle with inclined eccentricity is investigated. A semi-analytical method is proposed to model the unbalanced magnetic pull and the electromagnetic torque of a rotor with inclin...

Powerline interference (PLI) is a major source of interference in the acquisition of electroencephalogram (EEG) signal. Digital notch filters (DNFs) have been widely used to remove the PLI such that actual features, which are weak in energy and strongly connected to brain states, can be extracted explicitly. However, DNFs are mathematically impleme...

Recent advances in artificial intelligence (AI) technology has led to increasing interest in the development of AI-based tool condition monitoring (TCM) methods. However, achieving good performance using these methods relies heavily on large training samples, which is both expensive and difficult to obtain in practical TCM applications. This study...

Tool wear condition monitoring (TCM) is of great significance to ensure manufacturing quality in milling processes, and the development of deep learning (DL) in recent years has led to increasing interest in DL-based TCM methods. However, most of these DL-based methods rely on large training samples to achieve good performances, which is expensive....

Tools are the most vulnerable components in milling processes conducted using numerical control milling machines, and their wear condition directly influences work-product quality and operational safety. As such, tool wear estimation is an essential component of NC milling operations. This study addresses this issue by proposing an extreme learning...

The prediction of grinding force has great significance in improving grinding quality and efficiency. This paper presents a predictive force model in plunge facing grinding considering both the cutting mechanism of single grain and the random nature of wheel topography. The model includes cutting deformation force and frictional force, which mainly...

As an important research area of modern manufacturing, tool condition monitoring (TCM) has attracted much attention, especially artificial intelligence (AI)- based TCM method. However, the training samples obtained in practical experiments have the problem of sample missing and sample insufficiency. A numerical simulation- based TCM method is propo...

Robust bearing fault detection is significant to reduce the machinery down-time, and to prevent catastrophic failure. Many algorithms are proposed for the faults feature extraction, but it remains challenging to monitors the condition of the mechanical systems from the overwhelming interference noise contained signal in a short response time. To ad...

During the monitoring of high-speed milling,the anti-aliasing filter is of great significance for the test signal to truly reflect the cutting state of the machine tool. The cutting force measurement of high-speed milling is similar to the sum of a series modulated sinusoidal waves (MSWs). In the frequency domain, the energy concentrated on several...

We are organizing a Special Issue "AI-Based Condition Monitoring in Manufacturing Systems" in Mathematical Problems in Engineering (ISSN 1563-5147, IF 1.009, Indexed by SCIE, Ei Compendex, Scopus). The purpose of this Special Issue is to gather state-of-the-art research contributing to recent advances in the field of condition monitoring and fault...

Multiple modes of vibration are usually incorporated in a single record of vibration measurement in condition monitoring of rotating machinery. Wavelet transform is an effective tool to detect and isolate transient fault features from other interfering modes. The conventional dyadic wavelet transform decomposes the signal into wavelet subspaces wit...

Tool condition monitoring (TCM) in numerical control (NC) machines plays a significant role in ensuring manufacturing quality. TCM based on multiple sensors can provide more information related to tool condition and is a topic of great interest. A novel TCM method for milling processes based on a two-layer angle kernel extreme learning machine (TAK...

Ball screws are crucial for improving the reliability and interchangeability of transmission mechanical systems; however, existing contact measurement methods that utilise stylus contact are not efficient, which precludes their use for rapid in-situ geometry evaluation. This paper presents a vision-based two-stage method for rapid measurement of ke...

Robust identification of bearing health states is closely linked to timely condition monitoring and downtime reducing for rotating machinery. Although many proposed algorithms achieve extraordinary performances on feature extraction, uncertainty still remains for the bearing fault identification. To address this problem, this paper introduces a two...

As a novel representation method, two dimensional (2D) segmentation is gaining ground as an effective condition monitoring method due to its high-level information descriptional ability. However, the accuracy of extracting frequency information is still limited by the finite gray-level and the extraction ability of distinguishable texture for each...

Accurate tool condition monitoring (TCM) is essential for the development of fully automated milling processes. This is typically accomplished using indirect TCM methods that synthesize the information collected from one or more sensors to estimate tool condition based on machine learning approaches. Among the many sensor types available for conduc...

Detecting tool wear conditions in milling process is of significance to enhance the reliability of machining equipment. However, traditional methods have run into difficulties due to interference from strong noise and other unknown vibration sources. To solve this problem, an intrinsic timescale decomposition (ITD) technique is combined with a kern...

The noise cancellation in electrocardiogram (ECG) signal is very influential to distinguish the essential signal features masked by noises. The power line interference (PLI) is the main source of noise in most of bio-electric signals. Digital notch filters can be used to suppress the PLI in ECG signals. However, the problems of transient interferen...

Electrocardiogram (ECG) signal represents the electrical activity of the heart and playing an increasingly important role for practitioners to diagnose heart diseases. Widely available ECG data and machine learning algorithms present an opportunity to improve the accuracy of automated arrhythmia diagnosis. However, a comprehensive evaluation of mor...

Machined surfaces are rough from a microscopic perspective no matter how finely they are finished. Surface roughness is an important factor to consider during production quality control. Using modern techniques, surface roughness measurements are beneficial for improving machining quality. With optical imaging of machined surfaces as input, a convo...

Precise three-dimensional measurements of surfaces are significant in many fields. Usually, three-dimensional descriptions of the object surface have to be acquired by contact measure probe or other non-contact equipment. The paper proposed a novel surface reconstruction method that uses camera relative irradiance via the image gray-scale value inf...

Power generation using waste-gas is an effective and green way to reduce the emission of the harmful blast furnace gas (BFG) in pig-iron producing industry. Condition monitoring of mechanical structures in the BFG power plant is of vital importance to guarantee their safety and efficient operations. In this paper, we describe the detection of crack...

As a typical example of large and complex mechanical systems, rotating machinery is prone to diversified sorts of mechanical faults. Among these faults, one of the prominent causes of malfunction is generated in gear transmission chains. Although they can be collected via vibration signals, the fault signatures are always submerged in overwhelming...

The cooling system has emerged as an effective way to alleviate the excessive heat generation during dry cutting processes. In this paper, we investigated a novel type of internal cooling system, independent of additional mechanical accessories, as a promising cooling alternative. The proposed system is devised as connected internal fluid channels...

Nowadays, the modeling and simulation of grinding have become powerful tools in predicting the process performance and work results. However, common simulations focus on material removal process of abrasive grains and neglect deformations of machine structure. The grinding quality can be influenced by various factors, of which the process-machine i...

The metal surface topology contains abundant information related to the health states of the cutting tool as well as the cutting operation. In this paper, we attempt to adopt 2D digital images of the machined metal surface, acquired via non-contact photo-imaging techniques, as the monitoring media. A Wallis filter based dodging algorithm is applied...

This work presents a digital graphic scanning (DGS) method, based on computer scanning graphics, to generate a grinding profile avoiding the difficulties appeared from the complex equations of the contact line. First the enveloping surface between the forming tool (rotor) profile and its corresponding cutting locus was developed, then based on Bres...

In order to meet the technical requirements of grinding the circumferential cutting edge of indexable inserts, thermo-mechanical properties of bowl-shaped grinding wheel in high speed grinding process and the influence of dimension variations of the grinding wheel on machining accuracy were investigated. Firstly, the variation trends of the dimensi...