【正文】 同層次的架構(gòu)，在另一種情況下的故障和運營商的遠程故障診斷性能與經(jīng)驗評估。 診斷系統(tǒng) 該系統(tǒng)使用的是可重構(gòu)的雙機器人裝配系統(tǒng) [20]羅克韋爾自動化實驗室如圖 1 所示 。第一個是用于驗證是否在規(guī)格范圍之內(nèi)的基臺部的檢查站。站 3 和 4 是相同的裝配站，氣動，龍門式取放機器人 組裝釘入洞的 基礎(chǔ)部分。 圖 1 自動化流水線 實驗?zāi)繕?biāo) 為了確定如何構(gòu)建遠程診斷體系結(jié)構(gòu)，便于故障診斷人員進行遠程診斷和了解遠程故障診斷性能的因素的影響，建立了以下目標(biāo)： 要建立一個模型，以評估排除故障和其他組合下的性能故障，操作和架構(gòu) 要診斷的故障排除性能、故障的性質(zhì)與遠程診斷體系結(jié)構(gòu)的影響研究 與遠 程診斷體系結(jié)構(gòu)研究的影響比較，當(dāng)?shù)剡\營商的技術(shù)能力上的表現(xiàn) 要比較的專家和新手疑難排解故障排除策略 實驗變量 三個輸入（獨立）的變量被確定為：遠程診斷架構(gòu)（ X1），系統(tǒng)運營商（ X2），和自動化系統(tǒng)故障（ X3）。在本節(jié)將介紹這些變量的詳細描述。架構(gòu) 1 采用 0 級與 1 級結(jié)合的能力。架構(gòu) 3 在 1 級的基礎(chǔ)上，并結(jié)合 2 級和 3 級的一定的能力水平。它的功能是類似的討論 0 級型的架構(gòu) [2]，其中包括視頻，語音傳輸，靜態(tài)圖像，文本通信，安全的文件傳輸。它包括 1 級架構(gòu)的所有功能，同時還通過一個圖形界面的運行狀態(tài)的實時監(jiān)控。 系統(tǒng)運營商 隨著當(dāng)?shù)剡\營商的自動化系統(tǒng)，遠程故障診斷基于 PLC 的系統(tǒng)環(huán)境方面，兩種情況下可以配置： 算子的低技術(shù)知識（新手）：這里指的情況是，在操作員僅僅是該系統(tǒng)的用戶和沒有技術(shù)的背景來理解的操作的系統(tǒng)，電氣，電子，或機械子系統(tǒng)。 操作員有足夠的技術(shù)知識（工程師）：這是指在運營商的情況下所需的技術(shù)背景，了解系統(tǒng)的運行，到網(wǎng)上去找 PLC，學(xué)生采取了 PLC 和自動化的過程。這項研究將包括四個類型的故障：故障較低的機器人臂（硬件故障），故障關(guān)閉夾子（產(chǎn)品故 障），插入故障（任務(wù)失?。?，和失敗的挑選（組合軟件故障和容許誤差 ）。 附件 2：外文原文 Remote diagnosis design for a PLCbased automated system: 2evaluation of factors affecting remote diagnosis performance Ramnath Sekar amp。 Zhenhua Wu Received: 16 June 2021 / Accepted: 17 May 2021 SpringerVerlag London Limited 2021 Abstract Troubleshooting performance in fault diagnosis tasks is monly studied in various industrial applications. Several experiments were performed in previous studies to understand the ability of process interfaces to assist troubleshooters in local fault diagnosis while considering the effect of interface, nature of the failure, and the expertise of the troubleshooter. Although several remote diagnosis architectures have been proposed and standards have been developed for levels of remote diagnosis, the extent to which the design of a remote diagnosis architecture can assist a troubleshooter in diagnosis and the factors affecting remote troubleshooting performance have not been frequently addressed. The objective of this paper is to understand the factors that impact remote troubleshooting performance, including remote diagnosis architecture, type of failure, level of expertise of the remote troubleshooter, and skill level of the local operator. Experiments were performed in which troubleshooters used three levels of remote diagnosis architectures to diagnose different types of failures in a programmable logic controller based discrete automated assembly system while working with local engineer and novice operators. The results suggest that for diagnosis of failures related to measured or monitored system variables by remote expert troubleshooters, remote troubleshooting performance improved with the increase in the levels of the remote diagnosis architectures. In contrast, in diagnosis of these failures by novice troubleshooters, no significant difference was observed between the three architectures in terms of remote troubleshooting performance, and the novice troubleshooters experienced problems with managing the increased information available. The experts exhibited better information gathering capabilities in that they spent more time per information source and made fewer transitions between information sources while diagnosing failures. Failures unrelated to monitored system parameters resulted in significantly reduced remote troubleshooting performance with all three architectures in parison to the failures related to monitored system parameters for both expert and novice troubleshooters. The difference in terms of overall remote troubleshooting performance between engineer and novice operators was not found to be significant. Keywords ： Remote diagnosis。 Telemaintenance。 Programmable Logic Controller。s facility via work or modem connection [2], thus remotely monitor systems, diagnose faults, and bring the equipment into full productive state. With remote diagnosis technology [3], technical consulting is done via inter such that s, updates, drawings, diagrams, manuals, video, images, etc. can be exchanged among customers and manufacturers. Active information exchange, involving remote access to the controller of the system or the control station PC of the plant via the work is part of remote diagnosis. One of the major advantages of remote diagnosis is that troubleshooters including experts, system integrators, and experienced operators can share their knowledge, experience, and skills in working with unexpected situations to enhance system availability. This in turn helps reduce operation cost by reducing machine down time without having to physically visit the system site. Huge time and cost savings are thus achieved. Many remote diagnosis systems have been proposed and implemented along with diagnostics algorithms including neural work [4], fuzzy logic [5], and support vector machine [6] for different applications. For diagnosing faults in programmable logic controller (PLC)based automated systems, however, the aforementioned diagnosis algorithms may not be efficient, because PLCbased automated systems are typically discrete event systems (DES). A DES is a discretestate, eventdriven system。 this research [10] was published in an earlier issue of the International Journal of Advanced Manufacturing Technology. The purpose of the current study is to analyze the extent to which architectures, faults, and skill level of operators influence remote troubleshooting performance. Understanding of these facto