Zheng Liu

• PhD, D.Eng., M.Eng., B.Eng.
• Professor (Full) at University of British Columbia - Okanagan

Detecting mycotoxins such as deoxynivalenol, aflatoxins, and zearalenone in grains is crucial for ensuring crop safety and maintaining consumer health, both for humans and animals. These toxins pose serious health risks, affect the marketability of grains in international markets, and influence their economic value. Hence, this paper reviews the us...

Steel strips, renowned for their exceptional strength, durability, and impact resistance, are ubiquitous in various manufacturing sectors, notably aerospace, shipbuilding, and automotive industries. However, surface defects on these strips are inevitable due to various factors, including processing and environmental conditions. As a result, the eff...

In magnetic anomaly detection (MAD), the magnetic anomaly signals can be easily affected by noise in a harsh test environment, resulting in a low signal-to-noise ratio (SNR). Current MAD techniques prioritize SNR over signal characteristics and edge details, resulting in distorted signals. To tackle this issue, we propose a novel MAD method that em...

Corrosion is an irreversible form of damage to pipe materials, which leads to the degradation of their mechanical and chemical properties. Corrosion damage reduces the lifespan of materials and, in some cases, can even lead to catastrophic failures. Therefore, it is essential to detect corrosion damages and develop effective preventative measures t...

Detecting and accurately locating defects through nondestructive inspection (NDI) is critical for maintaining pipeline integrity. Employing multiple NDI techniques on the same pipeline enhances inspection reliability, especially when dealing with complex, degraded structures. However, misalignments between data acquired from different sensing modal...

Geomagnetic field data, a form of spatio-temporal data, holds significant importance in predicting earthquakes and magnetic storms. However, challenges arise due to missing data caused by factors like hardware failures and environmental interferences, hindering further research. In recent years, tensor-based data completion methods have garnered at...

The frequency of the free induction decay (FID) signal output from an Overhauser magnetometer sensor is proportional to the magnetic field to be measured. Due to the low initial signal-to-noise ratio (SNR), sensor tuning is necessary to suppress the noise and thus improve the frequency estimation accuracy. To improve the tuning performance in compl...

Metal workpiece imaging is of great significance for industrial nondestructive testing. However, current methods such as radiographic or electromagnetic induction, are easily influenced by background noise, which leads to a short detection range and a high false detection rate. To overcome this issue, this paper proposes a wide-focus imaging method...

The frequency of the free induction decay (FID) signal induced from an Overhauser magnetometer sensor is proportional to the magnetic field to be measured. Due to the low initial signal-to-noise ratio (SNR), sensor tuning is necessary to suppress the noise and thus improve the frequency estimation accuracy. To improve the tuning performance in comp...

This study presents a framework for distributed predictive maintenance using the BaSyx platform and edge computing devices. By integrating predictive maintenance algorithms, the system can estimate remaining useful life, detect early faults, and identify anomalies without prior failure data. The real-time processing capabilities of edge devices, co...

This is the abstract book of the papers accepted in the Third International Workshop on Big Data in Healthcare - BDH 2024. The workshop is organized In conjunction with Seventh IEEE International Conference on Multimedia Information Processing and Retrieval - IEEE MIPR 2024, San Jose USA, August 7 – 9, 2024.

Lightning strikes pose a significant challenge for aircraft and wind turbine blades with Carbon Fiber Reinforced Polymer (CFRP) structures, requiring reliable damage detection techniques. Non-destructive evaluation (NDE) methods, including X-ray and Ultrasonic Testing, are effective in identifying material damage in aircraft. However, X-ray require...

Real-time object detection is significant for industrial and research fields. On edge devices, a giant model is difficult to achieve the real-time detecting requirement, and a lightweight model built from a large number of the depth-wise separable convolutional could not achieve the sufficient accuracy. We introduce a new lightweight convolutional...

In recent years, intelligent fault diagnosis based on deep learning has achieved vigorous development due to its powerful feature representation ability. However, the data collected from industrial sites often contain different levels of noise, which makes it difficult to extract effective fault features, which seriously affects the performance of...

Purpose Deep learning‐based auto‐segmentation algorithms can improve clinical workflow by defining accurate regions of interest while reducing manual labor. Over the past decade, convolutional neural networks (CNNs) have become prominent in medical image segmentation applications. However, CNNs have limitations in learning long‐range spatial depend...

Entropy-based methods have shown promise in detecting dynamic changes in non-linear signals and have been widely applied in fault diagnosis for rotating machinery. However, these methods have limitations when it comes to capturing frequency-domain information of fault features, as they are primarily based on time-domain signals. To address this iss...

Damage identification in composite materials is an area of interest within the field of structural health monitoring. Damage identification in the field not only cares about recognition accuracy but also requires robustness to the variation of the input data. Deep learning methods to analyze guided wave signals collected by ultrasonic testing for d...

In the rapidly evolving landscape of Industry 4.0, digital twins have emerged as a transformative technology across various industrial sectors. This paper presents a comprehensive, in-depth review of digital twin models in terms of the concept and evolution, fundamental components and frameworks, and existing digital twin models based on their func...

Energy consumption of buildings is correlated with environmental factors such as weather and temperature. Developing a reliable energy forecasting model requires a large-scale, high-quality dataset. However, privacy regulations mandate that data be stored locally, prohibiting data sharing. As a solution, a Federated Building Energy Forecasting fram...

Surface defects directly affect the mechanical properties of industrial strip steel products. To evaluate the integrity of the strip steel surface, a channel-wise global Transformer-based dual-branch network (CGTD-Net) for strip steel surface defect detection, dubbed CGTD-Net, is proposed in this study. First, the strip steel surface images are pre...

Agriculture production is one of the fundamental contributors to a nation's economic development. Every year, plant diseases result in significant crop losses that threaten the global food supply chain. Early estimation of plant diseases could play an essential role in safeguarding crops and fostering economic growth. Recently, hyperspectral imagin...

Lempel–Ziv complexity (LZC), as a nonlinear feature in information science, has shown great promise in detecting correlations and capturing dynamic changes in single-channel time series. However, its application to multichannel data has been largely unexplored, while the complexity of real-world systems demands the utilization of data collected fro...

Recent advances in data-driven methods have significantly promoted intelligent fault diagnostics for varied industrial applications. However, due to the limitations of machine fault data and the varied scenarios in the context of industrial working conditions, existing diagnostic models can hardly achieve satisfactory results. In this study, we pro...

In recent years, intelligent fault diagnosis based on deep learning has achieved vigorous development thanks to its powerful feature representation ability. However, scarcity of high-quality data, especially samples under severe fault states, and variable operating conditions have limited the industrial application of intelligent fault diagnosis. T...

In the digital transformation of industry “Industry 4.0,” “Smart Factory,” “Digital Twins,” “Artificial Intelligence,” and “Internet of Things” are all terms used to indicate a transition toward a smart, networked, and autonomous industrial ecosystem. We are in a quickly evolving industrial revolution that is made possible by the increasing number...

Water is an essential element for the survival of human beings and a nation. Nowadays, water utilities perform regular inspections of the internal conditions of the pipelines via autonomous robotic platforms. The human operator then analyzes the recording of the platforms to identify the defects inside the water pipelines. This manual assessment pr...

Multiscale entropy-based methods have made great progress in the field of health condition monitoring and fault diagnosis of machines due to their powerful feature representation capabilities. However, existing multiscale entropy methods suffer from three major obstacles: high fluctuation under large scale-factor, loss of high-frequency information...

Magnetic anomaly detection (MAD) is one of the most effective methods for engineering and environmental geophysical exploration, and plays an important role in scientific research and human technological innovation. Although numerous systematic and valuable articles have been published on the topic of MAD, they only briefly summarized relevant rese...

It has become a common practice for many perceptual systems to integrate information from multiple sensors to improve the accuracy of object detection. For example, autonomous vehicles use visible light, and infrared (IR) information to ensure that the car can cope with complex weather conditions. However, the accuracy of the algorithm is usually a...

There is a positive correlation between wheat plant height and lodging, yield, and biomass. So, in precision agriculture, a high-throughput estimation of the wheat plant's height in terms of its spikes is essential. This study aims to develop a straightforward, cost-effective method for measuring the height of wheat plants using stereo cameras. To...

We are pleased to inform that we are organising 2nd International Workshop on Big Data in Healthcare in conjunction with IEEE MIPR 2023 going to held during August 30- Sepetember 1, 2023 at Singapore. The intent of this workshop is to bring together researchers, practitioners, and scientific communities to report and discuss the common challenges,...

The magnetic anomaly signal tends to be contaminated by ambient environmental noise due to the complexity and diversity in the field of magnetic anomaly detection (MAD). The current denoising methods are effective in improving the signal-to-noise ratio (SNR). However, most of them are only applicable to the Gaussian noise and perform poorly for the...

Cross-domain machinery fault diagnosis aims to transfer enriched diagnosis knowledge from a labeled source domain to a new unlabeled target domain. Most existing methods assume that the prior information on the fault modes of the target domain is known in advance. However, in engineering practice, prior knowledge of fault modes is rare in a new dom...

Due to their effectiveness in vibration-based fault feature extraction from bearings, entropy-based methods have become a hot research topic. Symbolic dynamic filtering reduces background noise in bearing signals, making it ideal for entropy analysis. However, the partitioning approach selection of symbolic dynamic filtering mainly depends on exper...

CNN-based fault detection approaches based on multisource signals have attracted increasing interest from the research community and industrial practices, thanks to the powerful feature representation capability of CNN and the rapid development of sensor technology. Various strategies have been applied in existing CNN-based diagnostic models to lea...

In the field of magnetic anomaly detection (MAD), the anomaly signal is easy to be submerged by ambient electromagnetic interference. Though the existing noise suppression methods can effectively improve the signal-to-noise ratio (SNR), there are still some intractable problems, such as signal distortion and boundary blur. To solve these problems,...

Gearbox has a compact structure, a stable transmission capability, and a high transmission efficiency. Thus, it is widely applied as a power transmission system in various applications, such as wind turbines, industrial machinery, aircraft, space vehicles, and land vehicles. The gearbox usually operates in harsh and non-stationary working environme...

In the era of industrial big data, data-driven methods based on deep learning have made a splash in the field of mechanical PHM. However, the performance of deep learning models is limited by the depth of networks while previous supervised learning methods can only be applied to a small amount of labeled data while most of data acquired in the fiel...

Multimodal imaging with visible and thermal sensors attracts much attention due to its robustness under challenging illumination conditions. Due to spectral differences, the visible and thermal images are typically misaligned, where image registration is necessary before high-level vision tasks such as information fusion, multimodal object detectio...

Sensor techniques and emerging CNN models have greatly facilitated the development of collaborative fault diagnosis. Existing CNN models apply different fusion schemes to achieve reliable fault identification based on multisensor data. Few CNN models, however, take into account both the intrinsic correlations and the distribution gap between differ...

Unsupervised cross-domain fault diagnosis for rotating machinery is of great practical significance for real-world industrial scenarios; however, most existing methods are developed based on the vibration signal from a single sensor. With the increasing complexity of industrial systems, multisensor collaborative monitoring has played an important r...

The frequency of a free induction decay (FID) signal from an industrial proton precession magnetometer (PPM) is proportional to the magnetic field strength. To achieve high-precision frequency estimation for an FID signal with a low signal-to-noise ratio (SNR), a long estimation period is always required which limits the application scenarios of th...

Reducing the vibration responses caused by the unbalance of the rotor is a crucial technology to ensure the safe and efficient operation of high-speed rotating machinery. For the flexible rotor system, the unbalance responses of rotors vary in different radial directions due to the anisotropic stiffness. The holospectrum technique using multi-senso...

The real estate market has a significant impact on people’s daily life. Therefore, it is crucial to understand the real estate market from both spatial and temporal perspectives, while there is still a lack of research in real estate industries. In this paper, a regional house price mining and forecasting (RHPMF) framework is proposed to help peopl...

Background: Automatic pulmonary artery-vein separation has considerable importance in the diagnosis and treatment of lung diseases. However, insufficient connectivity and spatial inconsistency have always been the problems of artery-vein separation. Methods: A novel automatic method for artery-vein separation in CT images is presented in this wo...

Fault diagnosis of rolling bearings has attracted extensive attention in industrial fields, which plays a vital role in guaranteeing the reliability, safety, and economical efficiency of mechanical systems. Traditional data-driven fault diagnosis methods require obtaining a dataset of full failure modes in advance as the training data. However, thi...

Non-contact sensing technology plays an important role in the health monitoring of the gearbox. However, a single non-contact measurement is challenging to achieve the simultaneous monitoring of both structural and non-structural damages. In order to explore the fusion mechanism of multi-sensor heterogeneous measurements, acoustic and thermal chara...

Traffic light optimization is one of the principal components to lessen the traffic flow and travel time in an urban area. The present article seeks to introduce a novel procedure to design the traffic lights in a city using evolutionary-based optimization algorithms in combination with an ontology-based driving behavior simulation framework. Accor...

The gearbox is widely applied as the mechanical transmission system of intelligent manufacturing systems, such as machine tools and robotics. The harsh working environments make the gear surface prone to wear. The progression of surface wear can bring severe failures to the gear tooth, including gear tooth root crack, surface spalling of gear tooth...

Fatigue pitting can reduce the gear surface durability and induce other severe failures, which will eventually lead to the complete loss of transmission function of the transmission system. Thus, monitoring fatigue pitting progression is vital to avoid unexpected economic losses and incidents. Thanks to the unique characteristics of the gear meshin...

For the existing coil vector magnetometers, the negative effect of the geomagnetic background interference generated by solar activity has hardly been considered, making it difficult to further improve the solution precision for the magnetic direction. To address this issue, we propose a novel method of mitigating the measurement noise arising from...

In the field of magnetic anomaly detection (MAD), the anomaly signal is easily submerged by ambient electromagnetic interference. Though the existing noise suppression methods can effectively improve the signal-to-noise ratio (SNR), there are still some intractable problems, such as signal distortion and boundary blur. To solve these problems, a no...

The theoretical developments of data-driven fault diagnosis methods have yielded fruitful achievements and significantly benefited industry practices. However, most methods are developed based on the assumption of data balance, which is incompatible with engineering scenarios. First, the normal state accounts for the majority of the equipment’s lif...

Graph neural networks are increasingly explored in the field of Prognostics and Health Management (PHM) due to their excellent performance when dealing with non-Euclidean data. However, current graph neural networks are mostly based on real domain modeling. In addition, existing graph construction methods rely on the prior positional relationship o...

In severe data imbalance scenarios, fault samples are generally scarce, challenging the health management of industrial machinery significantly. Generative adversarial network, a promising solution to solve the data imbalance problem, suffers from a negative overfitting issue when trained with few samples. To tackle challenges, this paper proposes...

Pipelines are economical and efficient modes of transporting oil and gas. Pipelines will inevitably confront various risk factors through their lifespan, which could lead to defects. Defects in pipelines can compromise the integrity of the pipeline systems and may result in catastrophic accidents. Thus, it is vital to conduct the integrity assessme...

In the aeromagnetic survey based on a fixed-wing unmanned aerial vehicle (UAV), the non-maneuvering magnetic interference generated by the magnetic components can significantly reduce the data quality of the airborne magnetometer. Aircraft layout modification is a standard method of addressing this problem. However, the existing layout modification...

Coil vector magnetometer is an advanced instrument that can perform integrated multi-element geomagnetic measurements and has excellent prospects for geoscience research and resource exploration applications. The attitude deviation is one of the main error sources of magnetic direction measurements of the coil vector magnetometer. The existing atti...

Automatically detecting and recognizing hazardous material placards using computer vision-based methods ensures safe operations and proper management of dangerous freight transportation. Deep learning-based object detection methods provide viable and practical solutions to varied applications. However, contemporary deep learning-based methods suffe...

Ultrasonic guided wave (UGW) based damage localization on plate-like composite structures plays a vital role in the structural health monitoring (SHM) of aircraft structures. Precisely locating the damage requires full utilization of high-dimensional UGW signals as well as low-dimensional transducer coordinates. However, current deep learning-based...

The rolling bearing of the servo motor is widely used in precision-controlled mechanical systems. It usually works at variable speed and load, possibly resulting in partial bearing failure. Meanwhile, the varying conditions may cause the smearing of classable features, increasing the diagnostic difficulty. To this end, an intelligent fault diagnosi...

The rotor system in the fault state generally shows apparent nonlinear behavior and complex dynamic characteristics. In general, the data collected from multiple sensors (namely, multichannel signals) is required to achieve accurate condition monitoring of the rotor system. Even though the traditional multivariate entropy method can extract fault c...

The absolute measurement of geomagnetic parameters can be applied directly not only in magnetic navigation and target detection but also in the correction of relative measurement. The absolute geomagnetic measurement generally adopts a combined measurement mode. Owing to differences in the principles of different magnetometers, it is difficult to c...

Wheat yield is positively correlated with the number of wheat spikes in the field, which is an essential index for plant breeders. To efficiently calculate this index, there is a high demand for precise and automatic plant phenotyping methods that quantify images. As a tool for estimating crop yields, machine vision is rapidly advancing. In the maj...

Precision farming has become a hot research topic in recent years due to the advancement of sensing technologies, increased computer performance, and advanced deep learning algorithms. As a result, several outstanding studies on deep learning applications to high-throughput phenotyping of wheat, one of the most demanding cereal crops on the planet,...