Tool Wear State Recognition with Deep Transfer Learning Based on Spindle Vibration for Milling Process

The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the toolwill generate significant noise and vibration, negatively impacting the accuracy of the forming and the surfaceintegrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wearstate andpromptly replace anyheavilyworn tools toguarantee thequality of the cutting.The conventional tool wearmonitoring models, which are based on machine learning, are specifically built for the intended cutting conditions.However, these models require retraining when the cutting conditions undergo any changes. This method has noapplication value if the cutting conditions frequently change. This manuscript proposes a method for monitoringtool wear basedonunsuperviseddeep transfer learning. Due to the similarity of the tool wear process under varyingworking conditions, a tool wear recognitionmodel that can adapt to both current and previous working conditionshas been developed by utilizing cutting monitoring data from history. To extract and classify cutting vibrationsignals, the unsupervised deep transfer learning network comprises a one-dimensional (1D) convolutional neuralnetwork (CNN) with a multi-layer perceptron (MLP). To achieve distribution alignment of deep features throughthe maximum mean discrepancy algorithm, a domain adaptive layer is embedded in the penultimate layer of thenetwork. A platformformonitoring tool wear during endmilling has been constructed. The proposedmethod wasverified through the execution of a full life test of end milling under multiple working conditions with a Cr12MoVsteel workpiece. Our experiments demonstrate that the transfer learning model maintains a classification accuracyof over 80%. In comparisonwith the most advanced tool wearmonitoring methods, the presentedmodel guaranteessuperior performance in the target domains.

Wind Turbine Spindle Operating State Recognition and Early Warning Driven by SCADA Data

An operating condition recognition approach of wind turbine spindle is proposed based on supervisory control and data acquisition(SCADA)normal data drive.Firstly,the SCADA raw data of wind turbine under full working conditions are cleaned and feature extracted.Then the spindle speed is employed as the output parameter,and the single and combined normal behavior model of the wind turbine spindle is constructed sequentially with the preprocessed data,with the evaluation indexes selected as the optimal model.Finally,calculating the spindle operation status index according to the slidingwindowprinciple,ascertaining the threshold value for identifying the abnormal spindle operation status by the hypothesis of small probability event,analyzing the 2.5 MW wind turbine SCADA data froma domestic wind field as a sample,The results show that the fault warning time of the early warningmodel is 5.7 h ahead of the actual fault occurrence time,as well as the identification and early warning of abnormal wind turbine spindle operationwithout abnormal data or a priori knowledge of related faults.

Wear State Recognition of Drills Based on K-means Cluster and Radial Basis Function Neural Network

Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps： first, decomposing cutting torque components from the original signals by wavelet packet decomposition （WPD）; second, extracting wavelet coefficients of different wear states （i.e., slight, normal, or severe wear） with signal features adapting to Welch spectrum. Finally, monitoring and recognition of the feature vectors of cutting torque signal are performed by using the K-means cluster and radial basis function neural network （RBFNN）. The experiments on different tool wears of the multivariable features reveal that the results of monitoring and recognition are significant and effective.

Affective State Recognition Using Thermal-Based Imaging: A Survey

The thermal-based imaging technique has recently attracted the attention of researchers who are interested in the recognition of human affects dueto its ability to measure the facial transient temperature, which is correlated withhuman affects and robustness against illumination changes. Therefore, studieshave increasingly used the thermal imaging as a potential and supplemental solution to overcome the challenges of visual (RGB) imaging, such as the variation oflight conditions and revealing original human affect. Moreover, the thermal-basedimaging has shown promising results in the detection of psychophysiological signals, such as pulse rate and respiration rate in a contactless and noninvasive way.This paper presents a brief review on human affects and focuses on the advantages and challenges of the thermal imaging technique. In addition, this paper discusses the stages of thermal-based human affective state recognition, such asdataset type, preprocessing stage, region of interest (ROI), feature descriptors,and classification approaches with a brief performance analysis based on a number of works in the literature. This analysis could help beginners in the thermalimaging and affective recognition domain to explore numerous approaches usedby researchers to construct an affective state system based on thermal imaging.

Theory,Method and Application of a State Recognition Based on Theory of Evidence

A new method of state recognition based on the theory of evidence was proposed. By this method, the plausible function which the sample awaiting recognition belongs to each category can be obtained through distance function. For the marginal samples,two or a batch of evidences can be combined and a new plausible function can be obtained by new evidence. Then the categories of samples can be determined according to plausibility function. This method provides a beder reasoning framework. The result proves the rate of recoghition correctness.

Advances in tissue state recognition in spinal surgery:a review

Spinal disease is an important cause of cervical discomfort,low back pain,radiating pain in the limbs,and neurogenic intermittent claudication,and its incidence is increasing annually.From the etiological viewpoint,these symptoms are directly caused by the compression of the spinal cord,nerve roots,and blood vessels and are most effectively treated with surgery.Spinal surgeries are primarily performed using two different techniques:spinal canal decompression and internal fixation.In the past,tactile sensation was the primary method used by surgeons to understand the state of the tissue within the operating area.However,this method has several disadvantages because of its subjectivity.Therefore,it has become the focus of spinal surgery research so as to strengthen the objectivity of tissue state recognition,improve the accuracy of safe area location,and avoid surgical injury to tissues.Aside from traditional imaging methods,surgical sensing techniques based on force,bioelectrical impedance,and other methods have been gradually developed and tested in the clinical setting.This article reviews the progress of different tissue state recognition methods in spinal surgery and summarizes their advantages and disadvantages.

State Key Pattern Recognition Laboratory

The Pattem Recognition Laboratory, set up byin 1984 and ratified as a state key lab in 1987, isattached to the CAS Institute of Automation (IA). The Laboraory’s founding director was Profes-sor Ma Songde, now the director of the Institute ofAntomation. Its current director is Professor TanTieniu.

Online Rail Fastener Detection Based on YOLO Network

Traveling by high-speed rail and railway transportation have become an important part of people’s life and social production.Track is the basic equipment of railway transportation,and its performance directly affects the service lifetime of railway lines and vehicles.The anomaly detection of rail fasteners is in a priority,while the traditional manual method is extremely inefficient and dangerous to workers.Therefore,this paper introduces efficient computer vision into the railway detection system not only to locate the normal fasteners,but also to recognize the fasteners states.To be more specific,this paper mainly studies the rail fastener detection based on improved You can Only Look Once version 5(YOLOv5)network,and completes the real-time classification of fastener states.The improved YOLOv5 network proposed contains five sections,which are Input,Backbone,Neck,Head Detector and a read-only Few-shot Example Learning module.The main purpose of this project is to improve the detection precision and shorten the detection time.Ultimately,the rail fastener detection system proposed in this paper is confirmed to be superior to other advanced algorithms.This model achieves on-line fastener detection by completing the“sampling-detection-recognition-warning”cycle of a single sample before the next image is sampled.Specifically,the mean average precision of model reaches 94.6%.And the model proposed reaches the speed of 12 ms per image in the deployment environment of NVIDIA GTX1080Ti GPU.

An Improved Minimum Distance Method Based on Artificial Neural Networks

MDM (minimum distance method) is a very popular algorithm in staterecognition. But it has a presupposition, that is, the distance within one class must be shorterenough than the distance between classes. When this presupposition is not satisfied, the method isno longer valid. In order to overcome the shortcomings of MDM, an improved minimum distance method(IMDM) based on ANN (artificial neural networks) is presented. The simulation results demonstratethat IMDM has two advantages, that is, the rate of recognition is faster and the accuracy ofrecognition is higher compared with MDM.

建立河流水质模型的状态空间分析法

该文导出了河流水质系统的离散时间状态空间模型和随机状态模型，采用最小二乘法和三种定阶方法，对水质模型中分布延迟系数和阶次进行估计。通过计算机仿真计算和建模实例，证实了辨识方法的正确性和状态空间模型的可靠性。