Data-Driven Approach for Condition Monitoring and Improving Power Output of Photovoltaic Systems

Increasing renewable energy targets globally has raised the requirement for the efficient and profitable operation of solar photovoltaic(PV)systems.In light of this requirement,this paper provides a path for evaluating the operating condition and improving the power output of the PV system in a grid integrated environment.To achieve this,different types of faults in grid-connected PV systems(GCPVs)and their impact on the energy loss associated with the electrical network are analyzed.A data-driven approach using neural networks(NNs)is proposed to achieve root cause analysis and localize the fault to the component level in the system.The localized fault condition is combined with a parallel operation of adaptive neurofuzzy inference units(ANFIUs)to develop a power mismatch-based control unit(PMCU)for improving the power output of the GCPV.To develop the proposed framework,a 10-kW single-phase GCPV is simulated for training the NN-based anomaly detection approach with 14 deviation signals.Further,the developed algorithm is combined with the PMCU implemented with the experimental setup of GCPV.The results identified 98.2%training accuracy and 43000 observations/sec prediction speed for the trained classifier,and improved power output with reduced voltage and current harmonics for the grid-connected PV operation.

Determination of an Appropriate Endoscopic Monitoring Interval for Patients with Gastric Precancerous Conditions in China

Objective Gastric precancerous conditions such as atrophic gastritis(AG)and intestinal metaplasia(IM)are considered independent risk factors for gastric cancer(GC).The suitable endoscopic monitoring interval is unclear when we attempt to prevent GC development.This study investigated the appropriate monitoring interval for AG/IM patients.Methods Totally,957 AG/IM patients who satisfied the criteria for evaluation between 2010 and 2020 were included in the study.Univariate and multivariate analyses were used to determine the risk factors for progression to high-grade intraepithelial neoplasia(HGIN)/GC in AG/IM patients,and to determine an appropriate endoscopic monitoring scheme.Results During follow-up,28 AG/IM patients developed gastric neoplasia lesions including gastric low-grade intraepithelial neoplasia(LGIN)(0.7%),HGIN(0.9%),and GC(1.3%).Multivariate analysis identified H.pylori infection(P=0.022)and extensive AG/IM lesions(P=0.002)as risk factors for HGIN/GC progression(P=0.025).Conclusion In our study,HGIN/GC was present in 2.2%of AG/IM patients.In AG/IM patients with extensive lesions,a 1–2-year surveillance interval is recommended for early detection of HIGN/GC in AG/IM patients with extensive lesions.

Wireless Self-Powered Vibration Sensor System for Intelligent Spindle Monitoring

In recent years,high-end equipment is widely used in industry and the accuracy requirements of the equipment have been risen year by year.During the machining process,the high-end equipment failure may have a great impact on the product quality.It is necessary to monitor the status of equipment and to predict fault diagnosis.At present,most of the condition monitoring devices for mechanical equipment have problems of large size,low precision and low energy utilization.A wireless self-powered intelligent spindle vibration acceleration sensor system based on piezoelectric energy harvesting is proposed.Based on rotor sensing technology,a sensor is made to mount on the tool holder and build the related circuit.Firstly,the energy management module collects the mechanical energy in the environment and converts the piezoelectric vibration energy into electric energy to provide 3.3 Vfor the subsequent circuit.The lithium battery supplies the system with additional power and monitors’the power of the energy storage circuit in real-time.Secondly,a three-axis acceleration sensor is used to collect,analyze and filter a series of signal processing operations of the vibration signal in the environment.The signal is sent to the upper computer by wireless transmission.The host computer outputs the corresponding X,Y,and Z channel waveforms and data under the condition of the spindle speed of 50∼2500 r/min with real-time monitoring.The KEIL5 platform is used to develop the system software.The small-size piezoelectric vibration sensor with high-speed,high-energy utilization,high accuracy,and easy installation is used for spindle monitoring.The experiment results show that the sensor system is available and practical.

Study of Fuzzy Neural Networks Model for System Condition Monitoring of AUV

A structure equivalent model of fuzzy-neural networks for system condition monitoring is proposed, whose outputs are the condition or the degree of fault occurring in some parts of the system. This network is composed of six layers of neurons,which represent the membership functions, fuzzy rules and outputs respectively. The structure parameters and weights are obtained by processing off-line learning, and the fuzzy rules are derived from the experience. The results of the computer simulation for the autonomous underwater vehicle condition monitoring based on this fuzzy-neural networks show that the network is efficient and feasible in gaining the condition information or the degree of fault of the two main propellers.

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.

Design of online control and monitoring software for the CPPF system in the CMS Level-1 trigger upgrade

The concentration preprocessing and fan-out(CPPF) system is one of the electronic subsystems of the upgraded Compact Muon Solenoid(CMS) Level-1 trigger system. It includes, in hardware, eight specially designed CPPF cards, one CMS card called AMC13, one commercial Micro-TCA Carrier HUB(MCH) card, and a MicroTCA shelf. Powerful online software is needed for the system, including providing reliable configuration and monitoring for the hardware, and a graphical interface for executing all actions and publishing monitoring messages.Further, to control and monitor the large amount of homogeneous hardware, the SoftWare Automating conTrol of Common Hardware(SWATCH) concept was proposed and developed. The SWATCH provides a generic structure and is flexible for customization. The structure includes a hardware access library based on the IPbus protocol, which assumes a virtual 32-bit address/32-bit data bus and builds a simple hardware access layer. Furthermore, the structure provides a graphical user interface, which is based on modern web technology and is accessible by web page. The CPPF controlling and monitoring online software was also customized from a common SWATCH cell, and provides afinite state machine(FSM) for configuring the entire CPPF hardware, and five monitoring objects for periodically collecting monitoring data from five main functional modules in the CPPF hardware. This paper introduces the details of the CPPF SWATCH cell development.

System Reliability Analysis of Redundant Condition Monitoring Systems

The development and application of new reliability models and methods are presented to analyze the system relia- bility of complex condition monitoring systems.The methods include a method analyzing failure modes of a type of redundant con- dition monitoring systems (RCMS) by invoking failure tree model,Markov modeling techniques for analyzing system reliability of RCMS,and methods for estimating Markov model parameters.Furthermore,a computing case is investigated and many conclu- sions upon this case are summarized.Results show that the method proposed here is practical and valuable for designing condition monitoring systems and their maintenance.

Study on Long-Distance Distributed Machine Condition Monitoring and Fault Diagnosis System

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DIA-based proteome profiling with PRM verification reveals the involvement of ER-associated protein processing in pollen abortion in Ogura CMS cabbage

Ogura cytoplasmic male sterility(Ogura CMS)is extensively applied in hybrid seed production in cruciferous crops.However,the posttranscriptional molecular basis of Ogura CMS in cruciferous crops remains elusive.Here,a data-independent acquisition-based proteomic approach coupled with a parallel reaction monitoring-based targeted proteomic assay was used to analyze the proteome dynamics of Ogura CMS cabbage line RM and its maintainer line RF during floral bud development to obtain insights into the mechanism underlying Ogura CMS in cruciferous crops.A total of 9162 proteins corresponding to 61464 peptides were identified in RM and RF floral buds.The proteomic fluctuation of RM was weaker than that of RF.Differences in protein expression between RM and RF gradually enlarged with floral bud development.Fifteen continually up-regulated and eight continually down-regulated proteins were found in RM relative to RF throughout floral bud development.Differentially expressed proteins between RM and RF during floral bud development were implicated in the endoplasmic reticulum(ER)-associated protein processing pathway,in which most of them exhibited down-regulated expression in RM.These data suggest that ER-associated protein processing may be involved in pollen abortion in Ogura CMS cabbage by inhibiting the expression of critical factors.Our findings not only deepen the understanding of the molecular mechanisms of Ogura CMS in cruciferous crops but also provide better guidance for applying Ogura CMS in the hybrid breeding of cruciferous crops.

Decentralized and overall condition monitoring system for large-scale mobile and complex equipment

It is an urgent project to realize online and overall condition monitoring and timely fault diagnosis for large-scale mobile and complex equipment. Moreover, most of the existing large-scale complex equipment has quite insufficient accessibility of examination, although it still has quite a long service life. The decentralized and overall condition monitoring, as a new concept, is proposed from the point of view of the whole system. A set of complex equipment is divided into several parts in terms of concrete equipment. Every part is processed via one detecting unit, and the main detecting unit is connected with other units. The management work and communications with the remote monitoring center have been taken on by it. Consequently, the difficulty of realizing a condition monitoring system and the complexity of processing information is reduced greatly. Furthermore, excellent maintainability of the condition monitoring system is obtained because of the modularization design. Through an application example, the design and realization of the decentralized and overall condition monitoring system is introduced specifically. Some advanced technologies, such as, micro control unit （MCU）, advanced RISC machines （ARM）, and control area network （CAN）, have been adopted in the system. The system＇s applicability for the existing large-scale mobile and complex equipment is tested.

Tool Condition Monitoring Based on Nonlinear Output Frequency Response Functions and Multivariate Control Chart

Tool condition monitoring(TCM)is a key technology for intelligent manufacturing.The objective is to monitor the tool operation status and detect tool breakage so that the tool can be changed in time to avoid significant damage to workpieces and reduce manufacturing costs.Recently,an innovative TCM approach based on sensor data modelling and model frequency analysis has been proposed.Different from traditional signal feature-based monitoring,the data from sensors are utilized to build a dynamic process model.Then,the nonlinear output frequency response functions,a concept which extends the linear system frequency response function to the nonlinear case,over the frequency range of the tooth passing frequency of the machining process are extracted to reveal tool health conditions.In order to extend the novel sensor data modelling and model frequency analysis to unsupervised condition monitoring of cutting tools,in the present study,a multivariate control chart is proposed for TCM based on the frequency domain properties of machining processes derived from the innovative sensor data modelling and model frequency analysis.The feature dimension is reduced by principal component analysis first.Then the moving average strategy is exploited to generate monitoring variables and overcome the effects of noises.The milling experiments of titanium alloys are conducted to verify the effectiveness of the proposed approach in detecting excessive flank wear of solid carbide end mills.The results demonstrate the advantages of the new approach over conventional TCM techniques and its potential in industrial applications.

The integrated monitoring system for running parameters of key mining equipment based on condition monitoring technology

An integrated monitoring system for running parameters of key mining equipmenton the basis of condition monitoring technology and modern communication networktechnology was developed.The system consists of a client computer with functions ofsignal acquisition and processing, and a host computer in the central control room.Thesignal acquisition module of the client computer can collect the running parameters fromvarious monitoring terminals in real-time.The DSP high-speed data processing system ofthe main control module can quickly achieve the numerical calculation for the collectedsignal.The signal modulation and signal demodulation are completed by the frequencyshift keying circuit and phase-locked loop frequency circuit, respectively.Finally, the signalis sent to the host computer for logic estimation and diagnostic analysis using the networkcommunication technology, which is helpful for technicians and managers to control therunning state of equipment.

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.

Research on Key Techniques of Condition Monitoring and Fault Diagnosing Systems of Machine Groups

This paper describes the development of the condition monitoring and faultdiagnosing system of a group of rotating machinery. The data management is performed by means ofdouble redundant data bases stored simultaneously in both the analyzing server and monitoringclient. In this way, high reliability of the storage of data is guaranteed. Condensation of trenddata releases much space resource of the hard disk. Diagnosing strategies orientated to differenttypical faults of rotating machinery are developed and incorporated into the system. Experimentalverification shows that the system is suitable and effective for condition monitoring and faultdiagnosing for a rotating machine group.

A Web-based Condition Monitoring and Diagnostic System of Rolling Mill

A web based condition monitoring and fault diagnosis system (CMAFDS) for the F2 finishing mill of the 2050 Hot Strip Mill was developed at a steel works. The features of the condition monitoring and fault diagnosis system based on the Web are analyzed in this paper. This paper also describes the main frame of the hardware and the software in the system and emphatically points out the function of the database management system(DBMS) based on the Web. It is proved that the web based CMAFDS is practical in technology and much superior to the CMAFDS based on other network technology in functions.

An Embedded Condition Monitoring and Fault Diagnosis System for Rotary Machines

An intelligent machine is the earnest aspiration of people. From the point of view to construct an intelligent machine with self-monitoring and self-diagnosis abilities, the technology for realizing an internet oriented embedded intelligent condition monitoring and fault diagnosis system for the rotating machine with remote monitoring, diagnosis, maintenance and upgrading functions is introduced systematically. Based on the DSP （ Digital Signal Processor） and embedded microcomputer, the system can measure and store the machine work status in real time, such as the rotating speed and vibration, etc. In the system, the DSP chip is used to do the fault signal processing and feature extraction, and the embedded microcomputer with a customized Linux operation system is used to realize the internet oriented remote software upgrading and system maintenance. Embedded fault diagnosis software based on mobile agent technology is also designed in the system, which can interconnect with the remote fault diagnosis center to realize the collaborative diagnosis. The embedded condition monitoring and fault diagnosis technology proposed in this paper will effectively improve the intelligence degree of the fault diagnosis system.

STUDY ON REALISTIC TECHNOLOGY OF CONDITION MONITORING AND FAULT DIAGNOSTIC SYSTEM FOR SHIPPING POWER DEVICES

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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.