帶式輸送機自動維護系統(tǒng)方法論文英文和翻譯

1、<p>　　Strategies for Automated Maintenance of Belt Conveyor Systems</p><p>　　Prof.dr.ir. Gabriel LodewijksDeIft University of Technology, the Netherlands</p><p><b>　　SUMMARY</b>

2、;</p><p>　　This paper discusses automation of maintenance of belt conveyor systems, in particular of idler rolls. Automation of maintenance is a promising alternative for outsourcing maintenance, in particul

3、ar when looking at the efficiency, accuracy, and costs. In order to optimise maintenance efforts, the concept of intelligent maintenance is introduced. The powered maintenance trolley that can travel autonomously over th

4、e structure of a belt conveyor is adapted as a platform of the maintenance system. O</p><p>　　INTRODUCTION</p><p>　　Today more and more companies outsource maintenance in an attempt to balance t

5、he budget and reduce the number of permanent staff members. Outsourcing maintenance however only works if the company that takes over maintenance employs well-trained and experienced personnel that stays on a specific jo

6、b for a considerable time. Unfortunately, reality is different and many companies have poor experiences with external companies performing maintenance.</p><p>　　In general, maintenance on belt conveyor syste

7、ms can be divided into inspection or condition monitoring of the total system and replacement and/or reparation (in short servicing) of its components. Most problems experienced with outsourcement of maintenance are asso

8、ciated with the inspection or condition monitoring of a system. It is not trivial to access the status of sometimes moving components of a belt conveyor. The same experienced person should therefore carry out inspections

9、 on a regular </p><p>　　To overcome operational problems caused by a lack of experience of external maintenance personnel, the inspection of belt conveyor components can be automated. In this way knowledge o

10、f for example wear rates and replacement schedules can be built up in a data base system. The external maintenance crew then can be used to replace the worn off components. Alternatively, replacement of components can be

11、 automated as well.</p><p>　　This paper discusses strategies and techniques for automated maintenance of belt conveyor systems. Section 2 defines the concept of intelligent maintenance, Section 3 discusses e

12、xisting inspection systems that can be used in automated maintenance systems. Section 4 discusses means of assessing the status of rotating components of belt conveyors based on vibration based monitoring concepts. Secti

13、on 5 presents a case study and section 6 finally lists the conclusions and recommendations.</p><p>　　INTELLIGENT MAINTENANCE</p><p>　　Maintenance on belt conveyor systems can be divided in condi

14、tion monitoring of the total system and servicing of its components. Condition monitoring is defined as the continuous or periodic measurement and interpretation of data to indicate the condition of a component to determ

15、ine the need for replacement or servicing.</p><p>　　Condition monitoring therefore deals with the acquirement of data (data acquisition or DAQ) from sensors, the interpretation of that data (data mining or D

16、AM) and with taking corrective actions (ACT) on components that are to fail, thus preventing fail systems from developing and propagating. The basic concept of condition monitoring is to identify subtle changes in operat

17、ion, such as increased vibration levels, that indicate a mechanical (or electrical) problem is starting to develop. These ear</p><p>　　There are four typical types of maintenance:</p><p>　　preve

18、ntive maintenance: calendar based, i.e. activities are planned depending on working hours or at certain time intervals (scheduled maintenance); it may be based on observed deterioration of components; nothing is repaired

19、 but preventive jobs are done. </p><p>　　random maintenance: opportunity based, i.e. maintenance is done when the opportunity arises; the decision to maintain a component based on opportunities may or may no

20、t be triggered by the condition of a component. </p><p>　　corrective maintenance: emergency based, i.e. repairing when a component malfunctions; this may cause a general shutdown of the system; the repair ac

21、tivity was not scheduled beforehand. </p><p>　　predictive maintenance: condition based, i.e. components are being monitored and when irregular factors are discovered, one waits until a maintenance opportunit

22、y arises; it is a planned and corrective maintenance. </p><p>　　From the above given four types of maintenance it is clear that only a predictive maintenance concept qualifies for application in an intellige

23、nt maintenance system that enables maintenance automation. Intelligence here is defined as the ability to make decisions based on information gathered through sensors in the equipment or provided by the control system of

24、 the total transport system.</p><p>　　Applied to belt conveyor systems the information gathered from a system is information on the life expectancy of individual components as for example idler rolls. This i

25、nformation leads to a decision either to inspect a certain idler station and its rolls more frequently or to change a roll for a new roll. Repairing in fact here means changing one roll for another. Whether or not a roll

26、 can be repaired and the effect of that on the belt conveyor’s performance is outside the scope of this study.</p><p>　　The main issue in this study is the question how an automated inspection strategy is af

27、fected by the accuracy of the data acquired. In theory there are two outer limits in predictive maintenance. The first is that no accurate information of the rolls is available at all, basically meaning that an assessmen

28、t of the remaining lifetime is made purely on the basis of historical data provided by the roll or bearing manufacturers (predictive maintenance based on statistics). The second is that during </p><p>　　EXIS

29、TING INSPECTION SYSTEMS</p><p>　　One problem faced with inspection or condition monitoring of components of belt conveyors, including the belt, pulleys and idler rolls, is that they rotate. Since the conditi

30、on of components like rolls and pulleys can only be assessed when they are rotating, only condition monitoring systems based on vibration analysis or acoustical monitoring can be used. The opposite holds for the belt. Th

31、e belt’s condition can only be inspected when the belt conveyor system is not operating. Either way, an in</p><p>　　The concept of a powered maintenance trolley is not new. An early example of a maintenance

32、trolley used on a belt conveyor system was the trolley used on the 100 km Phosboucraa overland system built by Krupp in the 70-ties to transport raw phosphate across a distance of 100 km from inside the west Sahara acros

33、s a desert of stones to the loading point on the coast. This long-distance conveyor system, consisting of belt systems with centre distances of 6.8 to 11,7 km. applied a maintenance trolley</p><p><b>　

34、　Figure 1</b></p><p>　　Powered maintenance trolley on Krupp system in Sahara.</p><p>　　A revitalised version of a maintenance trolley is shown in Figure 2. This concept, designed and devel

35、oped by CKIT of South Africa, is quite robust and stable. It has been installed on a number of pipe conveyor systems, both inside and outside South Africa. It has three inspection platforms and is supported on six points

36、 (vertical and transverse direction). Current systems are powered from the main platform by combustion engines. Today’s trolleys are all men operated and inspection and servicing </p><p>　　The concept of the

37、 maintenance trolley as developed by CKIT is adapted in this study as the platform for the further development of a fully automated maintenance facility. This development is divided in three stages.</p><p>　

38、　The first stage will be the development of a maintenance robot on the trolley, enabling both automated inspection and servicing. The design of such a robot, which is a research project carried out at the section of Tran

39、sport Engineering and Logistics of Delft University of Technology, is not considered in this paper. The second stage is the implementation of the automated inspection routines as will be described in Section 5. The third

40、 stage is the full integration of the automated maintenance t</p><p>　　DATA ACQUISITION AND MINING</p><p>　　Condition monitoring techniques generally include one or several alarms that go off wh

41、en a working point is exceeded or when a trend deviates from the expected values in time. References of the working points of signals are provided by knowledge-based systems and not by comparison with a model of the syst

42、em. Signals are acquired by sensor systems.</p><p>　　DATA ACQUISITION TECHNIQUES</p><p>　　Choosing the proper data acquisition technique has a large impact on the efficiency of the maintenance s

43、trategy. Good, reliable measurements, as well as proper analyses of the results of those measurements, are essential for reliable actions of the maintenance system. For rotating components most often a signal-based condi

44、tion monitoring system is applied based on vibration and/or acoustics measurement techniques. Another option is using force and torque measurements as a basis for condition mon</p><p>　　Spectral analysis is

45、an important tool in vibration based condition monitoring. In general vibration based monitoring means measuring acceleration levels using three-dimensional accelerometers. The signal acquired from these sensors therefor

46、e is acceleration as a function of time. In a spectral analysis this signal is transformed from the time domain to the frequency domain by applying a Fast Fourier Transform technique (FFT). With the signal in the frequen

47、cy domain the root source of the signal</p><p>　　Vibration analysis is one of the main forms of condition monitoring and, in general, is often applied in the industry. The spectral density of vibration level

48、s of a good working component will generally be low. When wear-out occurs, or when loads appear on specific components, then some small but notable changes will occur in the dynamical behaviour of the component. By makin

49、g these changes visible and analysing them, a diagnosis of the problem can be made.</p><p>　　The monitoring techniques used in practice can be divided into two main categories:</p><p>　　signal R

50、MS (Root Mean Square) based monitoring </p><p>　　detailed signal spectrum based monitoring </p><p>　　Acoustical analysis strongly resembles vibration analysis.</p><p>　　Data mining

51、follows after data acquisition. Data mining consists of three main steps. The first step is the detection of defects that is based on knowledge of the dynamics of the components monitored. The second step is data process

52、ing, transforming the 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.</p><p>　　DATA MINING

53、 I - DETECTION OF DEFECTS</p><p>　　In this paper the main focus is on bearings since bearings are, by far, the major source of the malfunctioning of rotating components including idler rolls. Obviously, idle

54、r 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

55、failures is discussed.</p><p>　　There are many ways in which bearing dynamics, which may lead to defects, can be classified. One of them is by defining component frequencies as a function of the rotating spe

56、ed 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

57、 dynamic effects in bearings are:</p><p>　　the cage rotational or the fundamental train frequency fcage: </p><p>　　the inner ring or ball pass inner ring frequency fir: </p><p>　　th

58、e outer ring or ball pass outer ring frequency for: </p><p>　　the rolling element or ball spin frequency fre: </p><p>　　Bearing defects show up as an increase in spectral density for defect rela

59、ted 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 rota

60、tional 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 ap</p>&l

61、t;p>　　outer ring defect: at f = nfor ± kfrot inner ring defect: at f = nfir ± kfrotrolling element defect: at f = nfre ± kfrotcage defect: at f = nfcage</p><p>　　where n is the numb

62、er of harmonics.</p><p>　　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 sp

63、ectrum.</p><p>　　DATA MINING II - TECHNIQUE OF DATA PROCESSING</p><p>　　Band enveloping is the process of transforming a vibration signal with small superimposed disturbances into isolated distu

64、rbance 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: high-pass f

65、iltering, rectification, and low-pass filtering.</p><p>　　As the energy of a disturbance compared to the energy of the sine wave is very low then the pulse is hardly detectable in the frequency spectrum. The

66、 first step therefore is to use a high-pass filter to filter out the (low frequency) sinusoidal component.</p><p>　　The remaining signal contains only the repetitive disturbances. The signal then is rectifie

67、d and passed through a low pass filter. The peak in the frequency spectrum then represents the defect frequency of the component that is defective. The pulses lose the high frequency components because of the low-pass fi

68、lter. The repetition period however remains.</p><p>　　DATA MINING III — DATA ANALYSIS</p><p>　　Data analysis can be quite complicated. If the scope of analysis is restricted to bearings and the

69、four identified possible defects as listed Section 4.2, 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)

70、can be used to identify the root cause. Knowing the root cause, for example outer ring problems, then the acquired signal level is compared to a data base identifying the seriousnes</p><p>　　AN INTELLIGENT M

71、AINTENANCE CONCEPT</p><p>　　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 eit

72、her statistics or the results of a detailed data analysis, which was introduced in Section 2. The technical lay-out of the maintenance system is based on the application of an automated maintenance trolley including a mo

73、nitoring and servicing robot as discussed in Section 3. The data acquisition and mining techniques us</p><p><b>　　MODEL</b></p><p>　　In the logistic model a number of elements are de

74、tailed including:</p><p>　　the belt conveyor itself, </p><p>　　the bearings, </p><p>　　the maintenance robot, </p><p>　　the inspection requirements, </p><p&g

75、t;　　the servicing aspects, </p><p>　　and the data analysis. </p><p>　　Belt conveyor</p><p>　　In the model the belt conveyor can be specified in terms of its length and the idler pit

76、ches. 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

77、 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 u</p><p><b&g

78、t;　　Bearings</b></p><p>　　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 (minimum a

79、nd maximum life length as specified by roll and bearing manufacturer). The chance of failure before reaching the minimum life length can be specified, again according to a uniform distribution. All distributions can be c

80、hanged for the middle and the side rolls of the carrying as well as the return idler sets.</p><p>　　Maintenance robot</p><p>　　The maintenance robot travels over the structure of the belt convey

81、or in the direction from the head to the tail at a constant speed. It is assumed that the robot is available 24 hours per day.</p><p>　　Inspection</p><p>　　On inspection of an idler set, the tot

82、al life length estimation of an individual roll is based either on historical data (statistics) or based on accurate vibration measurements. The total inspection time consists of a fixed set-up time and the inspection ti

83、me itself. All rolls in one idler set are inspected at the same time using a multiple sensor robot arm.</p><p><b>　　Service</b></p><p>　　If the maintenance robot decides to change a

84、roll then it is always replaced by a roll of the same type. The total reparation time consists of a fixed set-up time and the time for repairing or, in this case, changing out the idler roll.</p><p>　　Estima

85、tion of residual lifetimes</p><p>　　The robot estimates the lifetime of a roll using a Fourier analysis of the vibrations in the roll. For simulation of this process, a model with 2 parameters is used. The e

86、stimation is a sample from a normal distribution with as mean the lifetime of the roll. The deviation of this distribution determines the accuracy of the estimation and is controlled by the first parameter (d). With the

87、second parameter (f), a bias is introduced. The estimator becomes conservative, biased towards underestimatin</p><p>　　The estimator S1 is defined by:</p><p>　　A sample of X is drawn for the nor

88、mal distribution each time estimation is required. If f equals zero then estimator is unbiased. The probability that the estimator overestimates the lifetime is 50 %. With f > 0 the estimator becomes biased towards un

89、derestimating the lifetime.</p><p>　　Both deviation and safety factor are a fraction of the residual lifetime (L - A). This means that when the age of the roll reaches its actual lifetime, the deviation and

90、the bias of the estimator both become zero. When the age equals the lifetime, the estimator S1 is 100 % accurate.</p><p>　　The effect of different values for d and f on the behavior of the estimator can be s

91、een in Figure 3. If f equals zero, 50 % of the estimations are overestimating the lifetime. This could result in late replacement of a roll, which is unwanted. By increasing f, the estimation becomes biased, conservative

92、. However, if f is too large, chances are that the estimation will drop below the actual age (as marked by the line x=y). This will result in an unnecessary early replacement of the roll.</p><p>　　The proper

93、 value for d depends on the physical properties of the robot and must be determined experimentally. Then, the simulation model can be used to determine the optimal value for t.</p><p>　　Figure 3Behaviour of

94、 the estimator for different values of d and f.</p><p>　　PLANNING AND CONTROL</p><p>　　Maintenance strategies</p><p>　　In theory, the maintenance robot can perform inspections both

95、on regular as well as non-regular time intervals. In this study it was assumed that the robot is inspecting the conveyor belt at fixed time intervals. Whether or not the use of a non-regular time interval is beneficial d

96、epends among other things on the ratio between the time the trolley needs to travel the conveyor versus the inspection interval time. The travel time is not only determined by the length of the belt conveyor but by the&l

97、t;/p><p>　　The cycle interval time must be specified in case of regular timed inspection intervals. Each cycle, the robot travels the entire conveyor belt first forward to the end, then back the beginning of th

98、e belt. During the cycle, different strategies are possible. Four strategies are considered in this paper</p><p>　　Note that it is not explicitly assumed that the servicing function is performed by a mainten