帶式輸送機--英文和翻譯-資料下載頁

【導讀】本文論述了帶式輸送機系統維修自動化，特別是托輥。自動維護是一種很有前途。為了更有效優化自動維護工作，維修供電的臺車，能夠在一個自主帶式輸。送機的結構是作為維護系統平臺調整。在這臺上安裝振動分析的數據采集設備，法是視邏輯模擬模型而定，模擬模型是帶式輸送機設計布局的本身。型也能得出帶式輸送機其他組成部件精確的剩余壽命信息。少長期外包維修專業工作人員人數。這些問題不是不值得探討，有時移動帶式輸送機組件的狀態。同樣經驗的人應該進行定期檢查機器，以避免同樣問題出現。第2部分是智能維修的概念，部分是結論和建議。帶式輸送系統的維修，可分為條件和整個系統及其組成部分的監測服務。該數據，并采取糾正行動，從而防住系統故障的發展和普及。算預防工作完成。和影響帶式輸送機的工作狀況不是本文研究范圍。檢查或檢測帶式輸送機各部件狀況遇到一個麻煩，包括轉動皮帶、滑輪和托輥。有超出時間的預期值趨勢時報警器作出報警。

　　

【正文】 acquired data in data that is better fit for analysis. The third step is the actual analysis of the data itself required to make a decision to take certain actions. DATA MINING I DETECTION OF DEFECTS In this paper the main focus is on bearings since bearings are, by far, the major source of the malfunctioning of rotating ponents including idler rolls. Obviously, idler rolls can also fail as a result of shell wear. The mechanism of shell wear however differs from bearing wear and as such requires a different detection procedure. In this paper only the detection procedure for bearing failures is discussed. There are many ways in which bearing dynamics, which may lead to defects, can be classified. One of them is by defining ponent frequencies as a function of the rotating speed frot and of some geometry parameters including the number of rolling elements N, the diameter of the rolling elements D, the contact angle φ, and the bearing pitch diameter P. The frequencies identifying the four main dynamic effects in bearings are: the cage rotational or the fundamental train frequency fcage: the inner ring or ball pass inner ring frequency fir: the outer ring or ball pass outer ring frequency for: the rolling element or ball spin frequency fre: Bearing defects show up as an increase in spectral density for defect related frequencies. Bearing defect frequencies are a result of impacts due to the rolling elements passing over the defects as they pass through the loaded zone of a bearing. The defect frequencies, except for the cage rotational frequency, are surrounded by sidebands in a real signal. If the defect frequency originates from a signal that passed the loaded zone, there are k sidebands where k € N. The next frequencies could appear in a spectrum: outer ring defect: at f = nfor 177。 kfrot inner ring defect: at f = nfir 177。 kfrot rolling element defect: at f = nfre 177。 kfrot cage defect: at f = nfcage where n is the number of harmonics. As the defect is smaller, the measured acceleration signal is more like a pulse than like a sine wave and the energy content decreases while the defect frequency increases in the spectrum. DATA MINING II TECHNIQUE OF DATA PROCESSING Band enveloping is the process of transforming a vibration signal with small superimposed disturbances into isolated disturbance information. The main reason for using an envelope of a signal is that one can detect developing defects like small cracks in a very early stage. The process of band enveloping consists of three steps: highpass filtering, rectification, and lowpass filtering. As the energy of a disturbance pared to the energy of the sine wave is very low then the pulse is hardly detectable in the frequency spectrum. The first step therefore is to use a highpass filter to filter out the (low frequency) sinusoidal ponent. The remaining signal contains only the repetitive disturbances. The signal then is rectified and passed through a low pass filter. The peak in the frequency spectrum then represents the defect frequency of the ponent that is defective. The pulses lose the high frequency ponents because of the lowpass filter. The repetition period however remains. DATA MINING III — DATA ANALYSIS Data analysis can be quite plicated. If the scope of analysis is restricted to bearings and the four identified possible defects as listed Section , then the procedure can be as follows. First the frequency spectrum is scanned for anomalies. If peaks are detected in this spectrum then the equations (5) till (8) can be used to identify the root cause. Knowing the root cause, for example outer ring problems, then the acquired signal level is pared to a data base identifying the seriousness of the defect(s) and determining a proper course of action. Part of the latter determination is based on mon ifthenelse structures enabling a structured (and automated) analysis of possible causes and future effects on operation. If more than one possible cause for a defect is known, for example a specific signal can identify a defect in a bearing but also in the sensor itself, then confidence factors have to be applied to ruleout the most possible cause. AN INTELLIGENT MAINTENANCE CONCEPT In this section a concept for the logistic control of an intelligent maintenance system is given. The maintenance concept is based on the predictive maintenance concept, using either statistics or the results of a detailed data analysis, which was introduced in Section 2. The technical layout of the maintenance system is based on the application of an automated maintenance trolley including a monitoring and servicing robot as discussed in Section 3. The data acquisition and mining techniques used were discussed in the previous section. MODEL In the logistic model a number of elements are detailed including: ? the belt conveyor itself, ? the bearings, ? the maintenance robot, ? the inspection requirements, ? the servicing aspects, ? and the data analysis. Belt conveyor In the model the belt conveyor can be specified in terms of its length and the idler pitches. The number of idlers then is calculated automatically assuming that the pitch is constant. It is assumed that a carrying idler has 3 rolls and a return idler 2. Each roll has two bearings, which have a minimum life length as specified by the roll and bearing manufacturer. The number of the rolls that fail before the minimum life length can be specified. As a standard this number is 10%. If on a system used rolls, instead of new rolls, are installed then the program accounts for this effect by allocating remaining life lengths to individual rolls. Bearings The life length of a specific bearing in a roll is allocated via a tabularized distribution. Under and upper limits can be specified assuming a uniform distribution (mi