Degradation In Detection Performance Research Articles

Predictive Maintenance System for Rotating Machinery Onboard Ships for Detecting Performance Degradation

Maintenance of rotating machinery is crucial for extending the lifespan and increasing the reliability of equipment onboard ships. Presently, breakdown and preventive methodologies are used for the maintenance of equipment. Further, dataloggers collect critical machinery parameters, and parameter data is used for real-time parameter monitoring. The availability of such extensive monitoring data has also led to the adoption predictive maintenance methodologies in the industry, wherein machine learning-based analysis of recorded data is used to predict impending defects and prompt required maintenance. In this paper, we propose a predictive maintenance system that records data through a network of sensors installed over multiple electrical motor pump sets onboard the ship and uses statistical analysis to detect equipment degradation. Our system has been deployed onboard a ship to undertake real-time predictive maintenance of electrical motor pump sets used in firemain, AC plants, stabilizers, steering pumps and other auxiliary engine room machinery.

Hyperspectral Anomaly Detection With Tensor Average Rank and Piecewise Smoothness Constraints.

Anomaly detection in hyperspectral images (HSIs) has attracted considerable interest in the remote-sensing domain, which aims to identify pixels with different spectral and spatial features from their surroundings. Most of the existing anomaly detection methods convert the 3-D data cube to a 2-D matrix composed of independent spectral vectors, which destroys the intrinsic spatial correlation between the pixels and their surrounding pixels, thus leading to considerable degradation in detection performance. In this article, we develop a tensor-based anomaly detection algorithm that can effectively preserve the spatial-spectral information of the original data. We first separate the 3-D HSI data into a background tensor and an anomaly tensor. Then the tensor nuclear norm based on the tensor singular value decomposition (SVD) is exploited to characterize the global low rank existing in both the spectral and spatial directions of the background tensor. In addition, the total variation (TV) regularization is incorporated due to the piecewise smoothness. For the anomaly component, the l2.1 norm is exploited to promote the group sparsity of anomalous pixels. In order to improve the ability of the algorithm to distinguish the anomaly from the background, we design a robust background dictionary. We first split the HSI data into local clusters by leveraging their spectral similarity and spatial distance. Then we develop a simple but effective way based on the SVD to select representative pixels as atoms. The constructed background dictionary can effectively represent the background materials and eliminate anomalies. Experimental results obtained using several real hyperspectral datasets demonstrate the superiority of the proposed method compared with some state-of-the-art anomaly detection algorithms.

OFIDS : Online Learning-Enabled and Fingerprint-Based Intrusion Detection System in Controller Area Networks

As a widely used industrial field bus, the controller area network (CAN) lacks security mechanisms (e.g., encryption and authentication). With the development of network connectivity and the increase in external communication, CAN networks are vulnerable to security attacks (e.g., masquerade). Attackers can intrude on CAN networks to control CAN devices, causing security threats. A fingerprint-based intrusion detection system (IDS) in CAN networks can detect masquerade attacks by scanning the unique clock signals of CAN devices. However, most state-of-the-art fingerprint-based IDSs commonly use an analog-to-digital converter module with a low frequency of 60 MHz to sample CAN signals, lowering the accuracy of the trained fingerprint-based IDS model. In addition, almost all fingerprint-based IDSs are trained offline and then detected online in CAN networks, ignoring that system clock signals of hardware change over time, resulting in degraded detection performance. An intrusion detection system based on physical fingerprints can achieve effective identification of masquerade attacks. This paper proposes an online learning-enabled and fingerprint-based IDS (OFIDS) in CAN networks to increase the sampling frequency, simplify the sampling circuit, shorten the delay time, improve the detection response time, and increase the detection accuracy. OFIDS uses a high-speed comparator (i.e., TLV3501) and field-programmable gate arrays (FPGA; i.e., Xilinx ZYNQ-7010) to sample the CAN_High signal, achieving a low sampling delay time of 4.5 ns and a high sampling frequency of 1 GHz. The self-adaptability of the backpropagation neural network is taken advantage of and used to train the OFIDS model with a detection accuracy of 99.9992%. The OFIDS model is deployed to a CAN network prototype with five CAN devices (i.e., two Arduino UNO boards and three STM32 microcontrollers), where online learning is conducted with a low training time of less than 2 min to update the OFIDS model. Experimental results show that OFIDS can achieve at least 99.99% detection accuracy within 0.18 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> s in a CAN network prototype and can achieve 98% detection accuracy in a real vehicle.

PDHF: Effective phishing detection model combining optimal artificial and automatic deep features

Currently, the increasing number of high-volume phishing attacks is among the largest threats to networking environments on a daily basis. During such a severe attack, researchers prefer to extract numerous features to improve the accuracy of phishing detection. However, the redundant features that may exist in the extracted feature set may be adapted by phishing attackers, not only degrading detection performance but also shortening the effective time of the constructed detection models. To address these problems, this study proposes phishing detection based on hybrid features (PDHF), a novel phishing detection model based on a combination of optimal artificial and automatic deep learning features. The optimal artificial phishing features are obtained by removing redundant features based on the newly designed feature importance evaluation index and an improved bidirectional search algorithm. To extend the effective time of phishing detection, deep features are learned from URLs using a one-dimensional character convolutional neural network (CNN) and a disorderly quantized attention mechanism. The experimental results show that PDHF outperforms many state-of-the-art methods and achieves an accuracy of 0.9965, precision of 0.9942, recall of 0.9940, and F1-score of 0.9941. These results can help in the development of a security plug-in for clients, browsers, and various instant messaging tools that run on network edges, personal computers, smartphones, and other personal terminals.

Blind Quality Assessment of Images Containing Objects of Interest.

To monitor objects of interest, such as wildlife and people, image-capturing devices are used to collect a large number of images with and without objects of interest. As we are recording valuable information about the behavior and activity of objects, the quality of images containing objects of interest should be better than that of images without objects of interest, even if the former exhibits more severe distortion than the latter. However, according to current methods, quality assessments produce the opposite results. In this study, we propose an end-to-end model, named DETR-IQA (detection transformer image quality assessment), which extends the capability to perform object detection and blind image quality assessment (IQA) simultaneously by adding IQA heads comprising simple multi-layer perceptrons at the top of the DETRs (detection transformers) decoder. Using IQA heads, DETR-IQA carried out blind IQAs based on the weighted fusion of the distortion degree of the region of objects of interest and the other regions of the image; the predicted quality score of images containing objects of interest was generally greater than that of images without objects of interest. Currently, the subjective quality score of all public datasets is in accordance with the distortion of images and does not consider objects of interest. We manually extracted the images in which the five predefined classes of objects were the main contents of the largest authentic distortion dataset, KonIQ-10k, which was used as the experimental dataset. The experimental results show that with slight degradation in object detection performance and simple IQA heads, the values of PLCC and SRCC were 0.785 and 0.727, respectively, and exceeded those of some deep learning-based IQA models that are specially designed for only performing IQA. With the negligible increase in the computation and complexity of object detection and without a decrease in inference speeds, DETR-IQA can perform object detection and IQA via multi-tasking and substantially reduce the workload.

Fusion detection in distributed MIMO radar under hybrid-order Gaussian model

In this paper, we deal with the point-like target fusion detection in a partially homogeneous environment with distributed MIMO radar. Specifically, we consider the imperfect waveform separation problem, which means that the matched filter output contains not only the auto-correlation term of the current matched waveform but also the cross-correlation terms, called waveform residuals, with the remaining transmitted waveforms. To this end, a hybrid-order Gaussian (HOG) model is utilized, where the target amplitude is deterministic but unknown, and the waveform residuals obey the Gaussian distribution. Then two adaptive detectors are developed according to the GLRT and Wald test, named HOG-GLRT and HOG-Wald respectively. At the fusion detection stage, we focus on the problem of signal-to-noise ratio (SNR) diversity, i.e., the detection performance degradation due to the average weighting of spatial diversity channels when the echo SNR differs. Combined with the Model Order Selection criterion and multiple hypothesis test, two modified fusion detectors based on channel selection are proposed, named MHOG-GLRT and MHOG-Wald. Finally, the numerical simulation results show that the HOG-GLRT is sensitive against waveform residuals, while the HOG-Wald exhibits strong robustness. And it also demonstrates the effectiveness of the MHOG-GLRT and MHOG-Wald facing the extreme scene of SNR diversity.

Data-Driven Prognostics and Diagnostics of Industrial Machinery — A Turbofan Engine Case Study

A machine’s Remaining Useful Life (RUL) is the expected life or usage time remaining before the machine requires repair or replacement. In data-driven methods, typical RUL estimation is performed using models trained with health condition indicator values derived from measured system data. A significant challenge in developing an RUL estimation model is transforming large, multivariate, noisy sensor datasets into useful format(s) that make the data analysis and processing pipeline efficient and extract valuable condition indicators from the data. This work uses the N-CMAPSS dataset to explore options and implications for efficiently organizing and storing large time-series datasets to support prognostics and diagnostics applications. We extend the work to demonstrate a predictive maintenance workflow and solution to (1) detect and classify faults in a turbofan engine and (2) estimate the RUL once we detect performance degradation. Under data engineering, we investigate the impact of various file formats and file types on memory and execution time when dealing with large datasets like N-CMAPSS. We analyze, pre-process, and extract/engineer critical features from the transformed dataset by leveraging our understanding of gas turbines' operation (e.g., Brayton Cycle). We also analyze the performance of various engine submodules for different flight phases (climb, cruise, and descent). This work also explains an approach to down-sample the time series data without losing information relevant to our goals. Using the health condition indicators derived and synthesized in the data engineering stage, we train machine learning models for diagnostics (differentiate between healthy operation and seven different types of faults in the turbofan engine) and prognostics (RUL estimation).

Exploration on the application of electronic information technology in signal processing based on big data

Abstract Mobile phones are the most commonly used electronic devices in people’s daily life. The image, voice, and other information in these devices need to be processed through signal transmission. The role of signal processing is to process the acquired information in a certain way to get the final result. In order to ensure that the whole processing program can work normally, it is necessary to implement good control to achieve the desired effect. However, with the continuous progress and development of science and technology, its requirements are becoming increasingly strict. The traditional signal processing method is unreliable, has poor real time, and has error-prone characteristics, which can no longer meet the accuracy requirements of current information acquisition equipment. Therefore, people begin to study more complex and precise information processing methods and apply these algorithms to various advanced electronic devices to achieve better results. From the perspective of big data, electronic information technology is generated and developed based on massive data processing. It not only has a strong storage function but also has strong computing power and a wide range of application scenarios. It has strong applicability in real life. In this article, the signal to be processed was divided into several wavelet components in different frequency ranges by empirical mode decomposition technology, and then the signal was denoised by combining three wavelet denoising methods to obtain noise data with good signal-to-noise ratio and high classification accuracy. Finally, the corresponding feature information was extracted according to the signal-receiving model to improve the system recognition rate. This article compared the traditional signal processing methods with the signal processing approaches from the perspective of electronic information technology. The results showed that the processing method had a high computing speed and could better solve the problem of detection performance degradation caused by interference. User satisfaction had also increased by 2.87%, which showed that signal processing based on big data and information processing technology had broad application prospects in communication systems. The core of open computer science is to build a unified, efficient, and scalable computing platform based on massive data processing and use signal processing and computer technology to manage and optimize the scheduling of information resources to better meet various business needs.

Low-Cost Structural Monitoring of Analog Circuits for Secure and Reliable Operation

The need for digital calibration due to large variations in fine-geometry processes as well as the need for performance locking mechanisms to prevent IC piracy have resulted in the prevalent use of digital assistance for analog circuits. In order to ensure the secure and reliable operation of the system, the performance of the analog circuits needs to be monitored in the field with robust, low-cost methods. In this paper, we propose a method reliant on monitoring simple invariants for detecting performance degradation of digitally-assisted analog circuits. We demonstrate the proposed method on two circuits: a low drop-out voltage regulator (LDO) and an active low-pass filter. Extensive Monte-Carlo simulations confirm that the proposed method detects deviations in performance due to tampering.

Longitudinal Degradation of Pavement Marking Detectability for Mobile LiDAR Sensing Technology in Real-World Use

Recent advancements in vehicle automation and driver-assistance systems that detect pavement markings has increased the importance of the detectability of pavement markings through various sensor modalities across weather and road conditions. Among the sensing techniques, light detection and ranging (LiDAR) sensors have become popular for vehicle-automation applications. This study used low-cost mobile multi-beam LiDAR to assess the performance of several types of pavement marking materials installed on a limited-access highway in various conditions, and quantified the degradation in detection performance over three years. Four marking materials, HPS-8, polyurea, cold plastic, and sprayable thermoplastic, were analyzed in the current study. LiDAR reflectivity data extracted from a total of 210 passes through the test sections were analyzed. A new detectability score based on LiDAR intensity data was proposed to quantify the marking detectability. The results showed that the pavement marking detectability varied across the material types over the years. The results provide guidance for selecting materials and developing maintenance schedules when marking detectability by LiDAR is a concern.

A Novel Coherent Integration Algorithm for Maneuvering Target Detection Based on Symmetric Instantaneous Autocorrelation Function

Detection and parameter estimation of maneuvering targets having a jerking motion are some of the challenging problems for modern radar systems. Such targets usually introduce range migration (RM) and Doppler frequency migration (DFM) problems leading to serious performance degradation in detection. To address these problems, a novel coherent integration (CI) algorithm is proposed based on a new symmetric instantaneous autocorrelation function (NSIAF), which can be utilized to reduce the order on the slow time and to eliminate the linear range migration (LRM) first. Then, the jerk and acceleration of the target are estimated after applying the keystone transform (KT) and the scaled Fourier transform (SFT); both of these are then used to construct the reference function for matched filtering. Finally, CI and target detection can be accomplished by the scaled inverse Fourier transform (SCIFT) after matched filtering. Both simulation data (this work) and practical radar experiment data (data set of others) were processed to validate the proposed algorithm. Compared with other representative algorithms, our algorithm can achieve a good balance between computational complexity and detection performance.

A Novel Adaptive STFT-SFA Based Fault Detection Method for Nonstationary Processes

In the field of fault detection, the nonstationary characteristics caused by external disturbances of wind turbines and other reasons can mask the fault signals, while the inconsistent data distribution between training data and test data due to equipment loss and other reasons can lead to model mismatch problems, both of which can lead to the degradation of fault detection performance. In order to solve the above problems, a novel adaptive fault detection framework is proposed in this work. First, the stationary features of nonstationary variables are extracted based on short-time Fourier analysis, after which the features are combined with the stationary variables. Second, isolation-based anomaly detection using nearest-neighbor ensembles is introduced as monitoring metrics for designing the statistic for the slow feature analysis method. Then, the differences between online normal data and training data are calculated, the model update factor and update strategy are designed, and an adaptive fault detection framework based on short-time Fourier transform-slow feature analysis (STFT-SFA) is proposed. Finally, the effectiveness of the proposed fault detection framework is verified using the Tennessee-Eastman process and actual wind turbine data. The results show that the proposed STFT-SFA method has a 94.0% correct monitoring rate (CMR) for Tennessee Eastman process failures, and the proposed adaptive STFT-SFA method has a 95.6% CMR for wind turbine failures, which are better than other comparative fault detection methods.

Exploring better target for shadow detection

Shadow detection aims to identify shadow regions from images, which plays a significant role in scene understanding. Existing approaches tend to ignore the annotation noises in ground truths, which will be overfitted in the later training phase and potentially degrade detection performance. To alleviate the impact of such noisy labels, this work proposes a framework for robust shadow detection (RSD) by locating and correcting them. Specifically, we first introduce a noise-rate blind sample selection scheme based on the prediction-level stability to identify the reliable parts from all pixel-level samples. Next, we design a label correction strategy based on the graph convolutional network, which can propagate the label information between reliable and unreliable parts. Finally, we enable subsequent robust learning by using a new training target with fewer noisy labels for each image. Experimental results on public benchmarks (i.e., SBU, ISTD, UCF and CUHK-Shadow) show that our method can be favorable against SOTAs. Our source code is available at https://github.com/wuwen1994/RSD.

Multi-target CFAR detector based on compressed sensing radar system

ABSTRACT In the multi-target scenarios, we consider the problem of constant false alarm rate (CFAR) target detection. For the conventional CFAR algorithms, the multi-target detection performance is mainly limited by the interference target tolerance within the reference window. In this paper, we propose a local ordered statistic CFAR (OS-CFAR) target detector based on compressed sensing (CS) radar system to address the degradation of detection performance in multi-target scenarios. With the analog-information-converter (AIC), the radar intermediate frequency (IF) signal is compressively sampled into discrete linear measurements. At each detection stage, multiple signal components are generated by correlation tests between the sensing matrix and linear measurements. By support set merge and proxy pruning, the signal components with the largest energy are retained. These components are the echoes reflected from the targets. Since target components are output in decreasing order of correlation, the proposed detector only requires a local OS-CFAR decision in the interval where the least correlated target is located, rather than traversing the entire interval. By continuously updating the detection stage, all targets that satisfy the false alarm rate requirement can be screened out. Finally, the performance of the proposed detector in the multi-target scenarios is confirmed by simulation results.

Multi-Dimensional Spread Target Detection with Across Range-Doppler Unit Phenomenon Based on Generalized Radon-Fourier Transform

Severe phenomena of across range-Doppler unit (ARDU) and decoherence occur when radar detects high-speed and high-maneuvering targets, causing degradation in detection performance of traditional FFT radar detection methods. The improvement in radar resolution causes a multi-dimensional spread phenomenon, where different scattering centers of the target are distributed on different range units, along with motion parameters such as velocity and acceleration. Unfortunately, current radar detection methods focus solely on range spread targets and cannot handle multi-dimensional spread, leading to a significant decline in detection performance. To overcome this problem, this paper proposes several methods to achieve high detection performance for multi-dimensional spread target detection with ARDU phenomenon. Firstly, the generalized likelihood ratio test (GLRT) is derived, and the energy integration generalized Rayleigh Fourier transform (EI-GRFT) is introduced to improve the detection performance of range spread cross-unit targets. Additionally, the double-threshold based hybrid GRFT (DT-HGRFT) is presented as an enhancement of EI-GRFT, enabling long-time integration along slow time and integration among multiple scatters by using HGRFT and multi-dimensional sliding double-threshold detection, respectively. Furthermore, a method for joint detections of multiple DT-HGRFTs is provided to handle the case where the number of scattering centers of multi-dimensional spread targets is unknown. Finally, a detailed theoretical analysis of the performance of the proposed method is presented, along with extensive simulations and practical experiments to demonstrate the effectiveness of the proposed methods.

HAFNet: Hierarchical Attentive Fusion Network for Multispectral Pedestrian Detection

Multispectral pedestrian detection via visible and thermal image pairs has received widespread attention in recent years. It provides a promising multi-modality solution to address the challenges of pedestrian detection in low-light environments and occlusion situations. Most existing methods directly blend the results of the two modalities or combine the visible and thermal features via a linear interpolation. However, such fusion strategies tend to extract coarser features corresponding to the positions of different modalities, which may lead to degraded detection performance. To mitigate this, this paper proposes a novel and adaptive cross-modality fusion framework, named Hierarchical Attentive Fusion Network (HAFNet), which fully exploits the multispectral attention knowledge to inspire pedestrian detection in the decision-making process. Concretely, we introduce a Hierarchical Content-dependent Attentive Fusion (HCAF) module to extract top-level features as a guide to pixel-wise blending features of two modalities to enhance the quality of the feature representation and a plug-in multi-modality feature alignment (MFA) block to fine-tune the feature alignment of two modalities. Experiments on the challenging KAIST and CVC-14 datasets demonstrate the superior performance of our method with satisfactory speed.

Joint allocation of transmit-receive resource for MIMO-STAP

Aiming to address the issue of target detection performance degradation due to limited resources of airborne multiple-in multiple-out (MIMO) radar, a joint allocation method of transmit-receive spatial-temporal resources is developed to ameliorate the detection performance of MIMO radar-based space-time adaptive processing (STAP). A MIMO-STAP model containing space-time resources to be configured is firstly constructed. Then, with the criterion of maximizing the output signal-to-clutter-noise ratio (SCNR), a problem to jointly design the transmitting and receiving elements, transmitting pulses, baseband waveform, and receiving weight is formulated. To tackle the resultant complex nonlinear joint optimization issue, it is decomposed into four independent sub-problems based on the alternating iteration strategy, then the sub-problems are efficiently solved by corresponding optimization techniques, i.e., the optimal antenna pulse subset can be selected by employing the sequential convex approximation (SCA) method, the transmitting waveform is optimized using the semi-positive definite program (SDP) and randomization method, and the receiving weight is designed under the minimum variance distortionless response (MVDR) criteria, which work iteratively to achieve an effective solution to the joint optimization problem. Extensive simulation results verify the effectiveness of the developed algorithm.

Empirical Analysis of Autonomous Vehicle's LiDAR Detection Performance Degradation for Actual Road Driving in Rain and Fog.

Light detection and ranging (LiDAR) is widely used in autonomous vehicles to obtain precise 3D information about surrounding road environments. However, under bad weather conditions, such as rain, snow, and fog, LiDAR-detection performance is reduced. This effect has hardly been verified in actual road environments. In this study, tests were conducted with different precipitation levels (10, 20, 30, and 40 mm/h) and fog visibilities (50, 100, and 150 m) on actual roads. Square test objects (60 × 60 cm2) made of retroreflective film, aluminum, steel, black sheet, and plastic, commonly used in Korean road traffic signs, were investigated. Number of point clouds (NPC) and intensity (reflection value of points) were selected as LiDAR performance indicators. These indicators decreased with deteriorating weather in order of light rain (10-20 mm/h), weak fog (<150 m), intense rain (30-40 mm/h), and thick fog (≤50 m). Retroreflective film preserved at least 74% of the NPC under clear conditions with intense rain (30-40 mm/h) and thick fog (<50 m). Aluminum and steel showed non-observation for distances of 20-30 m under these conditions. ANOVA and post hoc tests suggested that these performance reductions were statistically significant. Such empirical tests should clarify the LiDAR performance degradation.

Radar coherent detection for hypersonic target based on shear transform and dechirp process

For hypersonic target detection, the range migration (RM) and Doppler frequency migration (DFM) often occur during coherent processing time, which would lead to integration loss and degrade detection performance. Aiming at these problems, this paper proposes a coherent integration method based on shear transform (ST) and dechirp process (DP), namely ST-DP. Specifically, the RM is eliminated by shearing the echo data plane, and then the DFM is compensated by dechirp process. Eventually, the Fourier transform (FT) is carried out in the slow time dimension to realize coherent integration. Compared with existed methods, the presented method has a coherent integration and detection performance close to that of GRFT without increasing the computational cost, while there is no blind speed sidelobe (BSSL) effect. Some simulation experiments are given to prove the practicality and efficacy of the presented method.

An improved YOLOv5s method based bruises detection on apples using cold excitation thermal images

Bruising is one of the key factors that causes postharvest losses, which decreases the economic efficiency of fruit. Nevertheless, the detection of bruises still relies mainly on manual work, which is strongly subjective with long labor time and low efficiency. Accordingly, it is necessary to design an efficient fruit bruise detection approach. Thermal imaging (TI) is a fast and effective nondestructive testing technology. However, the commonly applied thermal excitation TI-based bruise detection may lead to a decrease in the shelf life of the fruit. Therefore, this study uses apple as the research object, introduces cold excitation to improve the sensitivity of bruise detection, and then constructs a simple longwavelength infrared range (7.5–13 µm) TI system to acquire the thermal image of bruised apples. In addition, the low signal-to-noise ratio of thermal images also leads to detection performance degradation. Thus, the YOLOv5s network is applied and improved to achieve better detection. The specific methods are described as follows: (1) Since the thermal images have the problem of duplicated RGB data, group convolution is used to reduce the feature duplication computation. (2) The bottleneck structure of YOLOv5s is replaced by the ghost bottleneck (GB), and the number of bottlenecks is reduced to decrease the computational quantity of extracting redundant features of thermal images. (3) The shrinkage module is inserted into the GB, and the threshold is automatically obtained through two fully connected layers without relevant professional knowledge to eliminate noise in the features that may cause performance degradation. The F2 score, mAP and mAP50 of the proposed model are 97.76%, 86.24% and 98.08%, respectively, which are better than those of YOLOv5s. Moreover, the computation and the FPS of the proposed model are 1.31 GFLOPs and 160, which are 31.95% and 121.21% of those of the YOLOv5s, respectively.