Understanding geographical conditions monitoring: a perspective from China

The geographical condition is a very important component of a country’s national condition,and geographical conditions monitoring(GCM)has been of great concern to the Chinese government.GCM has a close relation with‘Digital China’and is a concrete embodiment of Digital China.This paper discusses the content and classification of GCM.In accordance with application areas,GCM can be divided into fundamental monitoring,thematic monitoring,and disaster monitoring.The application areas perspective includes the content of the three other perspectives,like the monitoring elements,the monitoring scope,and the monitoring cycle and fully reflects the essence of the GCM.Fundamental monitoring mainly focuses on monitoring all of the geographical elements,which provides a basis for follow-up thematic monitoring;thematic monitoring is a special type of designated subject monitoring that concerns the public or the government;disaster monitoring focuses on the dynamic monitoring of the pre-disaster and disaster periods for natural disasters.The monitoring results will provide timely information for governments so that they can meet management or decision-making requirements.A GCM case study in the area of the Qinghai−Tibet plateau was made,and some concrete conclusions were drawn.Finally,this paper presents some thoughts on conducting GCM.

VR-based digital twin for remote monitoring of mining equipment:Architecture and a case study

Background Traditional methods for monitoring mining equipment rely primarily on visual inspections,which are time-consuming,inefficient,and hazardous.This article introduces a novel approach to monitoring mission-critical systems and services in the mining industry by integrating virtual reality(VR)and digital twin(DT)technologies.VR-based DTs enable remote equipment monitoring,advanced analysis of machine health,enhanced visualization,and improved decision making.Methods This article presents an architecture for VR-based DT development,including the developmental stages,activities,and stakeholders involved.A case study on the condition monitoring of a conveyor belt using real-time synthetic vibration sensor data was conducted using the proposed methodology.The study demonstrated the application of the methodology in remote monitoring and identified the need for further development for implementation in active mining operations.The article also discusses interdisciplinarity,choice of tools,computational resources,time and cost,human involvement,user acceptance,frequency of inspection,multiuser environment,potential risks,and applications beyond the mining industry.Results The findings of this study provide a foundation for future research in the domain of VR-based DTs for remote equipment monitoring and a novel application area for VR in mining.

Statistical Models for Condition Monitoring and State of Health Estimation of Lithium-Ion Batteries for Ships

Battery systems are increasingly being used for powering ocean going ships,and the number of fully electric or hybrid ships relying on battery power for propulsion is growing.To ensure the safety of such ships,it is important to monitor the available energy that can be stored in the batteries,and classification societies typically require the state of health(SOH)to be verified by independent tests.This paper addresses statistical modeling of SOH for maritime lithium-ion batteries based on operational sensor data.Various methods for sensor-based,data-driven degradation monitoring will be presented,and advantages and challenges with the different approaches will be discussed.The different approaches include cumulative degradation models and snapshot models,models that need to be trained and models that need no prior training,and pure data-driven models and physics-informed models.Some of the methods only rely on measured data,such as current,voltage,and temperature,whereas others rely on derived quantities such as state of charge.Models include simple statistical models and more complicated machine learning techniques.Insight from this exploration will be important in establishing a framework for data-driven diagnostics and prognostics of maritime battery systems within the scope of classification societies.

Temporally Preserving Latent Variable Models:Offline and Online Training for Reconstruction and Interpretation of Fault Data for Gearbox Condition Monitoring

Latent variable models can effectively determine the condition of essential rotating machinery without needing labeled data.These models analyze vibration data via an unsupervised learning strategy.Temporal preservation is necessary to obtain an informative latent manifold for the fault diagnosis task.In a temporalpreserving context,two approaches exist to develop a condition-monitoring methodology:offline and online.For latent variable models,the available training modes are not different.While many traditional methods use offline training,online training can dynamically adjust the latent manifold,possibly leading to better fault signature extraction from the vibration data.This study explores online training using temporal-preserving latent variable models.Within online training,there are two main methods:one focuses on reconstructing data and the other on interpreting the data components.Both are considered to evaluate how they diagnose faults over time.Using two experimental datasets,the study confirms that models from both training modes can detect changes in machinery health and identify faults even under varying conditions.Importantly,the complementarity of offline and online models is emphasized,reassuring their versatility in fault diagnostics.Understanding the implications of the training approach and the available model formulations is crucial for further research in latent variable modelbased fault diagnostics.

AN INTELLIGENT TOOL CONDITION MONITORING SYSTEM USING FUZZY NEURAL NETWORKS

Reliable on line cutting tool conditioning monitoring is an essential feature of automatic machine tool and flexible manufacturing system (FMS) and computer integrated manufacturing system (CIMS). Recently artificial neural networks (ANNs) are used for this purpose in conjunction with suitable sensory systems. The present work in Norwegian University of Science and Technology (NTNU) uses back propagation neural networks (BP) and fuzzy neural networks (FNN) to process the cutting tool state data measured with force and acoustic emission (AE) sensors, and implements a valuable on line tool condition monitoring system using the ANNs. Different ANN structures are designed and investigated to estimate the tool wear state based on the fusion of acoustic emission and force signals. Finally, four case studies are introduced for the sensing and ANN processing of the tool wear states and the failures of the tool with practical experiment examples. The results indicate that a tool wear identification system can be achieved using the sensors integration with ANNs, and that ANNs provide a very effective method of implementing sensor integration for on line monitoring of tool wear states and abnormalities.

Working Condition Real-Time Monitoring Model of Lithium Ion Batteries Based on Distributed Parameter System and Single Particle Model

Lithium ion batteries are complicated distributed parameter systems that can be described preferably by partial differential equations and a field theory. To reduce the solution difficulty and the calculation amount, if a distributed parameter system is described by ordinary differential equations （ODE） during the analysis and the design of distributed parameter system, the reliability of the system description will be reduced, and the systemic errors will be introduced. Studies on working condition real-time monitoring can improve the security because the rechargeable LIBs are widely used in many electronic systems and electromechanical equipment. Single particle model （SPM） is the simplification of LIB under some approximations, and can estimate the working parameters of a LIB at the faster simulation speed. A LIB modelling algorithm based on PDEs and SPM is proposed to monitor the working condition of LIBs in real time. Although the lithium ion concentration is an unmeasurable distributed parameter in the anode of LIB, the working condition monitoring model can track the real time lithium ion concentration in the anode of LIB, and calculate the residual which is the difference between the ideal data and the measured data. A fault alarm can be triggered when the residual is beyond the preset threshold. A simulation example verifies that the effectiveness and the accuracy of the working condition real-time monitoring model of LIB based on PDEs and SPM.

Sparsity-Assisted Intelligent Condition Monitoring Method for Aero-engine Main Shaft Bearing

Weak feature extraction is of great importance for condition monitoring and intelligent diagnosis of aeroengine.Aimed at achieving intelligent diagnosis of aero-engine main shaft bearing,an enhanced sparsity-assisted intelligent condition monitoring method is proposed in this paper.Through analyzing the weakness of convex sparse model,i.e.the tradeoff between noise reduction and feature reconstruction,this paper proposes an enhanced-sparsity nonconvex regularized convex model based on Moreau envelope to achieve weak feature extraction.Accordingly,a sparsity-assisted deep convolutional variational autoencoders network is proposed,which achieves the intelligent identification of fault state through training denoised normal data.Finally,the effectiveness of the proposed method is verified through aero-engine bearing run-to-failure experiment.The comparison results show that the proposed method is good at abnormal pattern recognition,showing a good potential for weak fault intelligent diagnosis of aero-engine main shaft bearings.

Drill bit wear monitoring and failure prediction for mining automation

This article introduces a novel approach for tricone bit wear condition monitoring and failure prediction for surface mining applications.A successful bit health monitoring system is essential to achieve fully autonomous blasthole drilling.In this research in-situ vibration signals were analyzed in timefrequency domain and signals trend during tricone bit life span were investigated and introduced to support the development of artificial intelligence(AI)models.In addition to the signal statistical features,wavelet packet energy distribution proved to be a powerful indicator for bit wear assessment.Backpropagation artificial neural network(ANN)models were designed,trained and evaluated for bit state classification.Finally,an ANN architecture and feature vector were introduced to classify the bit condition and predict the bit failure.

Swarm-LSTM: Condition Monitoring of Gearbox Fault Diagnosis Based on Hybrid LSTM Deep Neural Network Optimized by Swarm Intelligence Algorithms

Nowadays,renewable energy has been emerging as the major source of energy and is driven by its aggressive expansion and falling costs.Most of the renewable energy sources involve turbines and their operation and maintenance are vital and a difficult task.Condition monitoring and fault diagnosis have seen remarkable and revolutionary up-gradation in approaches,practices and technology during the last decade.Turbines mostly do use a rotating type of machinery and analysis of those signals has been challenging to localize the defect.This paper proposes a new hybrid model wherein multiple swarm intelligence models have been evaluated to optimize the conventional Long Short-Term Memory(LSTM)model in classifying the faults from the vibration signals data acquired from the gearbox.This helps to analyze the performance and behavioral patterns of the system more effectively and efficiently which helps to suggest for replacement of the unit with higher precision.The results have demonstrated that the proposed hybrid modeling approach is effective in classifying the faults of the gearbox from the time series data and achieve higher diagnostic accuracy in comparison to the conventional LSTM methods.

Turbopump Condition Monitoring Using Incremental Clustering and One-class Support Vector Machine

Turbopump condition monitoring is a significant approach to ensure the safety of liquid rocket engine （LRE）.Because of lack of fault samples,a monitoring system cannot be trained on all possible condition patterns.Thus it is important to differentiate abnormal or unknown patterns from normal pattern with novelty detection methods.One-class support vector machine （OCSVM） that has been commonly used for novelty detection cannot deal well with large scale samples.In order to model the normal pattern of the turbopump with OCSVM and so as to monitor the condition of the turbopump,a monitoring method that integrates OCSVM with incremental clustering is presented.In this method,the incremental clustering is used for sample reduction by extracting representative vectors from a large training set.The representative vectors are supposed to distribute uniformly in the object region and fulfill the region.And training OCSVM on these representative vectors yields a novelty detector.By applying this method to the analysis of the turbopump＇s historical test data,it shows that the incremental clustering algorithm can extract 91 representative points from more than 36 000 training vectors,and the OCSVM detector trained on these 91 representative points can recognize spikes in vibration signals caused by different abnormal events such as vane shedding,rub-impact and sensor faults.This monitoring method does not need fault samples during training as classical recognition methods.The method resolves the learning problem of large samples and is an alternative method for condition monitoring of the LRE turbopump.

A system for underground road condition monitoring

Poor road conditions in underground mine tunnels can lead to decreased production efficiency and increased wear on production vehicles. A prototype system for road condition monitoring is presented in this paper to counteract this. The system consists of three components i.e. localization, road monitoring, and scheduling. The localization of vehicles is performed using a Rao-Blackwellized extended particle filter, combining vehicle mounted sensors with signal strengths of Wi Fi access points. Two methods for road monitoring are described: a Kalman filter used together with a model of the vehicle suspension system, and a relative condition measure based on the power spectral density. Lastly, a method for taking automatic action on an ill-conditioned road segment is proposed in the form of a rescheduling algorithm.The scheduling algorithm is based on the large neighborhood search and is used to integrate road service activities in the short-term production schedule while minimizing introduced production disturbances.The system is demonstrated on experimental data collected in a Swedish underground mine.

Online Condition Monitoring of Gripper Cylinder in TBM Based on EMD Method

The gripper cylinder that provides braced force for Tunnel Boring Machine （TBM） might fail due to severe vibration when the TBM excavates in the tunnel. Early fault diagnosis of the gripper cylinder is important for the safety and efficiency of the whole tunneling project. In this paper, an online condition monitoring system based on the Empirical Mode Decomposition （EMD） method is estab- lished for fault diagnosis of the gripper cylinder while TBM is working. Firstly, the lumped mass parameter model of the gripper cylinder is established considering the influence of the variable stiffness at the rock interface, the equivalent stiffness of the oil, the seals, and the copper guide sleeve. The dynamic performance of the gripper cylinder is investigated to provide basis for its health condition evaluation. Then, the EMD method is applied to identify the characteristic frequencies of the gripper cylinder for fault diagnosis and a field test is used to verify the accuracy of the EMD method for detection of the characteristic frequencies. Furthermore, the contact stiff- ness at the interface between the barrel and the rod is calculated with Hertz theory and the relationship between the natural frequency and the stiffness varying with the health condition of the cylinder is simulated based on the dynamic model. The simulation shows that the character- istic frequencies decrease with the increasing clearance between the barrel and the rod, thus the defects could be indicated by monitoring the natural frequency. Finally, a health condition management system of the gripper cylin- der based on the vibration signal and the EMD method is established, which could ensure the safety of TBM.

CONDITION MONITOR OF DEEP-HOLE DRILLING BASED ON MULTI-SENSOR INFORMATION FUSION

A condition monitoring method of deep-hole drilling based on multi-sensor information fusion is discussed. The signal of vibration and cutting force are collected when the condition of deep-hole drilling on stainless steel 0Cr17Ni4Cu4Nb is normal or abnormal. Four eigenvectors are extracted on time-domain and frequency-domain analysis of the signals. Then the four eigenvectors are combined and sent to neural networks to dispose. The fusion results indicate that multi-sensor information fusion is superior to single-sensor information, and that cutting force signal can reflect the condition of cutting tool better than vibration signal.

A graphic monitoring method for electric power of VVVF hydraulic system

In order to online monitor the running state of variable voltage and variable frequency(VVVF)hydraulic system,this paper presents a graphic monitoring method that fuses the information of variable frequency electric parameters.This paper first analyzes how the voltage and current of the motor stator change with the operation conditions of VVVF hydraulic system.As a result,we draw the relationship between the electric parameters(voltage and current)and power frequency.Then,the signals of the voltage and current are fused as dynamic figures based on the idea of Lissajous figures,and the values of the electric parameters are related to the features of the dynamic figures.Rigorous theoretical analysis establishes the function between the electric power of the variable frequency motor(VFM)and the features of the plotted dynamic figures including area of diagram,area of bounding rectangle,tilt angle,etc.Finally,the effectiveness of the proposed method is verified by two cases,in which the speed of VFM and the load of VVVF hydraulic system are changed.The results show that the increase of the speed of VFM enhances its three-phase electric power,but reduces the tilt angle of the plotted dynamic figures.In addition,as the load of VVVF hydraulic system is increased,the three-phase electric power of VFM and the tilt angle of the plotted dynamic figures are both increased.This paper provides a new way to online monitor the running state of VVVF hydraulic system.

New Monitoring Technologies for Overhead Contact Line at 400 km.h-1

Various technologies have recently been developed for high-speed railways, in order to boost commercial speeds from 300 km.h： to 400 km.h-1. Among these technologies, this paper introduces the 400 km-h-1 class current collection performance evaluation methods that have been developed and demonstrated by Korea. Specifically, this paper reports details of the video-based monitoring techniques that have been adopted to inspect the stability of overhead contact line （OCL） components at 400 km.h-1 without direct contact with any components of the power supply system. Unlike conventional OCL monitoring systems, which detect contact wire positions using either laser sensors or line cameras, the developed system measures parameters in the active state by video data. According to experimental results that were obtained at a field-test site established at a commercial line, it is claimed that the proposed mea- surement system is capable of effectively measuring OCL parameters.

Bio-inspired computational techniques based on advanced condition monitoring

The application of bio-inspired computational techniques to the field of condition monitoring is addressed. First, the bio-inspired computational techniques are briefly addressed; the advantages and disadvantages of these computational methods are made clear. Then, the roles of condition monitoring in the predictive maintenance and failures prediction and the development trends of condition monitoring are discussed. Finally, a case study on the condition monitoring of grinding machine is described, which shows the application of bio-inspired computational technique to a practical condition monitoring system.

Massive Power Device Condition Monitoring Data Feature Extraction and Clustering Analysis using MapReduce and Graph Model

Effective storage,processing and analyzing of power device condition monitoring data faces enormous challenges.A framework is proposed that can support both MapReduce and Graph for massive monitoring data analysis at the same time based on Aliyun DTplus platform.First,power device condition monitoring data storage based on MaxCompute table and parallel permutation entropy feature extraction based on MaxCompute MapReduce are designed and implemented on DTplus platform.Then,Graph based k-means algorithm is implemented and used for massive condition monitoring data clustering analysis.Finally,performance tests are performed to compare the execution time between serial program and parallel program.Performance is analyzed from CPU cores consumption,memory utilization and parallel granularity.Experimental results show that the designed framework and parallel algorithms can efficiently process massive power device condition monitoring data.

Drilling signals analysis for tricone bit condition monitoring

This paper presents a novel approach to investigate the relations between drilling signals and bit wear condition in real world full-scale mining operations.This research addresses the increasing demand for automation in mining to increase the efficiency,safety,and ability to work in harsh environments.A crucial issue in fully autonomous unmanned drilling is to have a system to detect the bit wear condition through the drilling signals analysis in real time.In this work,based on extensive field studies,a novel qualitative method for tricone bit wear state classification is developed and introduced.The relations between drilling vibration as well as electric motor current signals and bit wear are investigated and bit failure vibration frequencies,regardless of the geological conditions,are introduced.Bit failure frequencies are experimentally investigated and analytically calculated.Finally,the effect of bit design parameters on the failure frequencies is presented for the application of bit wear condition monitoring and bit failure prediction.

3D Microdisplacement Monitoring of Large Aircraft Assembly with Automated In Situ Calibration

Three-dimensional(3D)microdisplacement monitoring plays a crucial role in the assembly of large aircraft.This paper presents a broadly applicable high-precision online 3D microdisplacement monitoring method and system based on proximity sensors as well as a corresponding in situ calibration method,which can be applied under various extreme working conditions encountered in the aircraft assembly process,such as compact and obstructed spaces.A 3D monitoring model is first established to achieve 3D microdisplacement monitoring based only on the one-dimensional distances measured by proximity sensors,which concerns the extrinsic sensor parameters,such as the probe base point(PBP)and the unit displacement vector(UDV).Then,a calibration method is employed to obtain these extrinsic parameters with high precision by combining spatial transformation principles and weighted optimization.Finally,calibration and monitoring experiments performed for a tailplane assembly process are reported.The calibration precision for the PBP is better than±10 lm in the X and Y directions and±2 lm in the Z direction,and the calibration precision for the UDV is better than 0.07°.Moreover,the accuracy of the 3D microdisplacement monitoring system can reach±15 lm.In general,this paper provides new insights into the modeling and calibration of 3D microdisplacement monitoring based on proximity sensors and a precise,efficient,and low-cost technical means for performing related measurements in compact spaces during the aircraft assembly process.

Method of Monitoring Wearing and Breakage States of Cutting Tools Based on Mahalanobis Distance Features

The Mahalanobis distance features proposed by P.C.Mahalanobis, an Indian statistician, can be used in an automatic on-line cutting tool condition monitoring process based on digital image processing. In this paper, a new method of obtaining Mahalanobis distance features from a tool image is proposed. The key of calculating Mahalanobis distance is appropriately dividing the object into several component sets. Firstly, a technique is proposed that can automatically divide the component groups for calculating Mahalanobis distance based on the gray level of wearing or breakage regions in a tool image. The wearing region can be divided into high gray level component group and the tool-blade into low one. Then, the relation between Mahalanobis distance features of component groups and tool conditions is investigated. The results indicate that the high brightness region on the flank surface of the turning tool will change with its abrasion change and if the tool is heavily abraded, the area of high brightness will increase apparently. The Mahalanobis distance features of high gray level component group are related with wearing state of tool and low gray level component group correlated with breakage of tool. The experimental results show that the abrasion of the tool’s flank surface affected the Mahalanobis distances of high brightness component of the tool and the pixels of high brightness component set. Compared with the changes of them, we found that the Mahalanobis distance of high brightness component of the tool was more sensitive to the abrasion of cutting tool than the area of high brightness component set of the tool. Here we found that the relative changing rate of the area of high brightness component set was not quite obvious and it was ranging from 2% to 15%, while the relative changing rate of the Mahalanobis distance in table 1 ranges from 13.9% to 47%. It is 3 times higher than the changing rate of the area.