Abstract:
A method of identifying abnormal operation of an industrial machine includes the step of determining statistical parameters from a plurality of samples of characteristic parameter(s) of known similar industrial machines in normal operation and storing them, the statistical parameters defining a statistical range of values of the characteristic parameter(s) for normal operation of the industrial machine. The characteristic parameter(s) of a machine being monitored are sampled and a determination ( 702 ) is made as to whether sampled characteristic parameter(s) falls within the statistical range of values for normal operation of the industrial machine. If the sampled characteristic parameter falls outside the statistical range of values for normal operation of the industrial machine an alarm signal is generated ( 706 ).

Description:
BACKGROUND OF THE INVENTION 
       [0001]    The present invention relates to a system for identifying abnormal operation of an industrial machine, for example, a packaging machine of the type used to package consumer products such as cans and bottles into multiple packaged cartons. The present invention also relates to a method of identifying abnormal operation of an industrial machine for the same purpose. 
         [0002]    The majority of known packaging machines are dedicated machines which can construct only one size or type of carton. Therefore, modern bottling plants are required to use several packaging machines to package different carton types. Some packaging machines are capable of packaging different types or sizes of cartons. All such machines require adjustment when switching from one size or type of carton to another. 
         [0003]    Packaging machines will typically package approximately 60,000 to 200,000 articles per hour and are required to run continuously for long periods of time. A machine failure means that the machine cannot be used (known as “down time”), which is an expensive delay in a bottling plant. Such a delay will usually result in down time for the entire bottling line, not just the packaging machine, particularly if problems have arisen. 
       SUMMARY OF THE INVENTION 
       [0004]    According to a first aspect of the present invention, there is provided an apparatus for identifying abnormal operation of an industrial machine, the apparatus comprising a sampling unit arranged to sample at least one characteristic parameter of the industrial machine, a storage device for storing predetermined statistical parameters determined from a plurality of samples of the characteristic parameter(s) of known similar industrial machines in normal operation, the statistical parameters defining a statistical range of values of the at least one characteristic parameter for normal operation of the industrial machine, and a processing unit coupled to a storage device and to the sampling unit for determining whether the at least one sampled characteristic parameter falls within the statistical range of values for normal operation of the industrial machine and for generating an alarm signal if the at least one sampled characteristic parameter falls outside the statistical range of values for normal operation of the industrial machine. 
         [0005]    In a preferred embodiment, the processing unit scales the at least one sampled characteristic parameter to the statistical range of values for normal operation of the industrial machine prior to the determination by the processing unit whether the at least one sampled characteristic parameter is within the statistical range of values for normal operation of the industrial machine. 
         [0006]    The processing unit preferably generates the alarm signal depending on how far from the statistical range of values for normal operation of the industrial machine the at least one sampled characteristic parameter is determined to be. 
         [0007]    The processing unit preferably generates the alarm signal depending on a degree of error of the statistical range of values for normal operation of the industrial machine. 
         [0008]    Preferably, the processing unit scales the samples of the characteristic parameter(s) of the known similar industrial machines in normal operation so that they correspond to each other prior to determining the statistical parameters defining the statistical range of values of the at least one characteristic parameter for normal operation of the packaging machine. 
         [0009]    The industrial machine may be a packaging machine and the at least one characteristic parameter preferably comprises a signal corresponding, in use, to torque values of a servo-motor used in the industrial machine. 
         [0010]    Preferably, the predetermined statistical parameters are determined so that the statistical range of values defines a Normal Distribution Curve, wherein the predetermined statistical parameters are the mean and the variance or standard deviation. 
         [0011]    According to a second aspect, the invention provides a method of identifying abnormal operation of an industrial machine, the method comprising the steps of sampling at least one characteristic parameter of the industrial machine, retrieving previously stored statistical parameters determined from a plurality of samples of the characteristic parameter(s) of known similar industrial machines in normal operation, the statistical parameters defining a statistical range of values of the at least one characteristic parameter for normal operation of the industrial machine, determining whether the at least one sampled characteristic parameter falls within the statistical range of values for normal operation of the industrial machine, and generating an alarm signal if the at least one sampled characteristic parameter falls outside the statistical range of values for normal operation of the industrial machine. 
         [0012]    In a preferred embodiment, the method, further comprises the step of scaling the at least one sampled characteristic parameter to the statistical range of values for normal operation of the industrial machine prior to the step of determining whether the at least one sampled characteristic parameter is within the statistical range of values for normal operation of the industrial machine. 
         [0013]    The step of generating the alarm signal preferably depends on how far from the statistical range of values for normal operation of the industrial machine the at least one sampled characteristic parameter is determined to be. 
         [0014]    The step of generating the alarm signal preferably depends on a degree of error of the statistical range of values for normal operation of the industrial machine. 
         [0015]    The method preferably further comprises the step of scaling the samples of the characteristic parameter(s) of the known similar industrial machines in normal operation so that they correspond to each other prior to determining the statistical parameters defining the statistical range of values of the at least one characteristic parameter for normal operation of the packaging machine. 
         [0016]    Preferably, the predetermined statistical parameters are determined so that the statistical range of values defines a Normal Distribution Curve, wherein the predetermined statistical parameters are the mean and the variance or standard deviation. 
         [0017]    According to a further aspect of the present invention, there is provided a computer program element comprising computer program means to make a computer execute the method described above. Preferably, the computer program element as is embodied on a computer readable medium. 
         [0018]    It is thus possible to provide an apparatus for identifying abnormal operation of an industrial machine that overcomes the technical and commercial disadvantages of known systems. In particular, it is possible to provide an alarm signal to an operator to provide an indication that preventative maintenance for likely problems prior to any catastrophic failure of the machine may be necessary. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0019]    One embodiment of the invention will now be more fully described, by way of example, with reference to the drawings, of which: 
           [0020]      FIG. 1  is a schematic diagram of part of a packaging system including a diagnostic apparatus constituting an embodiment of the present invention; 
           [0021]      FIG. 2  is a schematic block diagram showing the inputs and outputs of the controller used in the diagnostic apparatus of  FIG. 1 ; 
           [0022]      FIG. 3  is a schematic diagram of the controller used in the diagnostic apparatus of  FIG. 1 ; and 
           [0023]      FIGS. 4 and 5  are flow diagrams of a data processing method for use by the controller of  FIG. 2 . 
           [0024]      FIG. 6  is a schematic diagram of a packaging system in which the present invention could be utilized. 
       
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
       [0025]    Throughout the following description, identical reference numerals shall be used to identify like parts. 
         [0026]    Referring to the drawings and in particular  FIG. 1  there is shown a system for integrating electrical and mechanical data and information technology in a packaging machine for improving productivity by forecasting and scheduling maintenance so that down time will not adversely impact production and market needs. The system can be employed generally on servo machines. 
         [0027]    The system comprises a controller  100  fitted to a packaging machine (not shown), but is usually incorporated into the existing control means. The controller  100  comprises an input device  102 , an output device  104  and a processing unit  106  that supports a user interface presented by the output device  104 . 
         [0028]    In order to perform condition maintenance, the system includes elements to diagnose problems. To achieve this, there further comprises a number of sensors  108  for monitoring various physical characteristic parameters. The physical parameters can be processed in order to provide additional characteristic parameters, as shown in  FIG. 1 . For example, a signal corresponding to a torque of a motor can undergo spectral analysis, an amplitude at a specific frequency revealed by the spectral analysis being of use as a parameter in a diagnostic process. Whilst, in at least one example of the present invention contained herein, sensors are employed in order to probe physical parameters, direct evaluation of physical parameters by a device constituting a sensor is possible. An example of such a sensor is a servo-motor as it is able to provide a signal corresponding to the torque of the servo-motor. 
         [0029]    For example, to monitor the various chain or belt assemblies, the chain tension  110  is monitored by measuring and processing the torque of the servo motor driving each chain. 
         [0030]    Preferably, the lubrication  112  is analysed by measuring the servo motor torque to diagnose for poor lubrication. 
         [0031]    This system is also used to diagnose a ‘tight spot’  114 . In a packaging machine, the ‘tight spot’ occurs when the package binds with one of the guides or moving parts on a conveyor or chain due to glass, paper, dust, glue, etc. which will result in the conveyor chain/belt jolting. 
         [0032]    Sensors may also be used to monitor one or more of chain wear  116 , bearing wear  118  and/or belt wear  120 , again by monitoring the servo motor torque to diagnose one assembly chains or belts. Referring to  FIG. 2 , for bearing wear analysis, a noise detection device  300  can be used in addition to, or as an alternative, to locate the particular position of a worn bearing. 
         [0033]    Optionally, visual information  302  about the condition of the machine, for example monitoring star wheel condition, jam induced with an article, is recorded by high speed cameras and fed to the controller  100  where a file is generated and saved in the hard disk of a PC within the system. 
         [0034]    In some embodiments, the signal from the sensor  108  will be filtered through known electronic filters  304  to reduce the background noise in the signal. 
         [0035]    Pre-programmed statistical parameters  306  for the various characteristic parameters being monitored are entered into the controller by pre-programming the system. The statistical parameters can be used as inputs for a computing system in order to evaluate the level of a specific problem, for example the chain tension evaluated using specific parameters and compared with upper and lower tolerance limits. The manner in which these pre-programmed statistical parameters are provided will be described in more detail below. 
         [0036]    Information from the servo motor sensors  308 , detected noise from the noise detection device  300  and visual information  302  is input into the control processor  106  and compared to the pre-programmed statistical parameters for each servo motor or machine assembly or module. If the input measurement from the sensors is not within a predetermined range or tolerance limit, then the control processor  106  will issue an alert message  310  and the measurement compared to various known parameters for faults in the machine so as to display the fault. For example, if a chain is subjected to the tight spot, the torque measurement will indicate that there are a number of spikes at regular intermittent intervals and the processor will display an alert message. If the chain tension deviates either above or below the predetermined range, this will indicate the tension of the chain is too loose or too tight. Again, a message is communicated to the user via the display. 
         [0037]    The operator will then intervene to correct the problem, or will monitor it more closely until scheduled maintenance. 
         [0038]    Optionally, the controller  100  may include a fail safe monitoring parameter so that if there is a serious problem, for example the measurements exceed pre-programmed safe working parameters, the controller will output a signal to automatically shut down the machine  314 . 
         [0039]    With certain parameters it is possible to automatically correct  312  the defect and various auto-correction devices are employed in the machine. In the illustrated embodiment of  FIG. 1 , the system includes a chain tensioner  122  controlled by the controller to be automatically introduced or moved thereby to increase or decrease the tension of the chain so as to return the servo-motor torque to within the pre-programmed range. Similarly, if it appears that the lubrication has deteriorated then micro-sprayers  124  are switched on by the controller  100  to lubricate the chains automatically and without the need for turning the machine off. If the problem is caused by a part that is worn and needs replacing, spare parts can be automatically ordered  316 . 
         [0040]    The information recorded by the controller  100  is stored on hard disc or other storage medium to be used to monitor the performance of the machine remotely from the packaging plant. Remote monitoring is achieved by coupling the controller  100  to a communications network  126  via a first communications link  128 . A server  130  is coupled to the communications network via a second communications link  132 . In the present example, the communications network is the Internet and so the controller  100  is capable of communicating packets of data with the server  130  which are routed through the Internet to a remote monitor. 
         [0041]    Referring to  FIG. 6 , there is shown a packaging system  400  in which the present invention could be utilised comprising a packaging machine  401  to which a first servo-motor  402 , a second servo-motor  404  and a third servo-motor  406  are coupled. A first driver unit  408 , a second driver unit  410  and a third driver unit  412  are coupled to the first, second and third servo-motors  402 ,  404 ,  406  respectively. In this example the first, second and third driver units  408 ,  410 ,  412  are SAM Smart Digital Drives of the type manufactured by Inmotion™ Technologies, although it will be appreciated that other suitable drivers can be used. 
         [0042]    Each of the first, second and third driver units  408 ,  410 ,  412  is coupled to a data bus  413 , the data bus  413  also being coupled to a driver management unit  414 . In this example, the driver management unit  414  is a Programmable Axis Manager (PAM) manufactured by Inmotion™ Technologies, although it will again be appreciated that any suitable driver management equipment can be employed. 
         [0043]    The PAM  414  supports a real-time task  415  that periodically samples a driving signal issued by any one or more of the first, second or third driver units  408 ,  410 ,  412  respectively to the first, second or third servo-motors  402 ,  404 ,  406 . The task  415  is activated, for example, every 10 ms if a sampling frequency of, for example, 100 Hz is required. The driving signals sampled by the task  415  also correspond to torque of the respective servo-motor. 
         [0044]    The PAM  414  is coupled to a Local Area Network (LAN)  416 , the LAN  416  being coupled to a Programmable Logic Controller  418  and a supervising computer  420 . In this example, the supervising computer  420  is a Personal Computer (PC). 
         [0045]    Referring to  FIG. 3 , the controller  100  comprises a processing unit or processor  500 , to which one or more input devices  502 , such as a keyboard and/or a mouse, and an output device  504  such as a display, are coupled. The processor  500  is also coupled to an Input/Output (I/O) port  506 , the I/O port  506  being coupled, in this example, to a port (not shown) of a LAN. 
         [0046]    A first storage device, for example a volatile memory, such as Random Access Memory (RAM)  508 , is coupled to the processor  500 . A second storage device, for example a non-volatile memory, such as Read Only Memory (ROM)  510 , is also coupled to the processor  500 . As is common with most PCs, the processor  500  is also coupled to a third, re-writable non-volatile, storage device, for example, a so-called hard drive, or Hard Disc Drive (HDD)  512 . The hard drive  512 , in this example, stores, inter alia, a first database  514 , a second database  516 , and a third database  518 . However, content of the first, second and third databases  514 ,  516 ,  518  need not be stored in a formal database structure provided by many well-known software packages, and can instead be stored, for example, as a simple look-up table. 
         [0047]    In operation ( FIG. 4 ), the controller  100  supports a monitoring cycle and a diagnosis cycle in order to identify abnormal or potentially abnormal operation of the packaging machine. 
         [0048]    With respect to  FIG. 4 , the controller  100  identifies and selects (step  600 ) a first parameter, for example a first servo-motor from a plurality of servo-motors in the packaging machine to monitor over a predetermined period of time at a predetermined sampling rate. The controller  100  then interrogates a driver management unit for samples of a first driving signal issued to the first servo-motor. The samples of the first driving signal so obtained (step  602 ) are then communicated to the controller  100 , the first driving signal issued to the first servo-motor by the first driver unit corresponding to a first torque exerted by the first servo-motor. Similarly, a second driving signal and a third driving signal respectively issued by the second and third driving units respectively correspond to second and third torques exerted by the second and third servo-motors. 
         [0049]    The sample of the first driving signal is subsequently stored (step  604 ) by the supervising computer in the first database  514 . After storing the sample of the first driving signal, the controller  100  determines (step  606 ) if the period over which the first driving signal is sampled has expired. If the period has not expired the controller  100  obtains (step  608 ) another sample of the first driving signal from the driver management unit in respect of a subsequent sampling period and stores (step  604 ) this most recent sample. 
         [0050]    If the period over which the driving signal is sampled has expired, the controller  100  determines (step  610 ) if driving signals imposed upon other servo-motors, such as the second or third servo-motors need to be sampled. If, in this example, the second or the third servo-motor still needs to be monitored, the controller  100  selects (step  612 ) one of the second or the third servo-motors for monitoring. The above-described sampling procedure is then repeated for the driving signal issued to the next selected servo-motor. Indeed, the above process of selection of servo-motors is repeated until all of the servo-motors have been monitored. The above monitoring procedure is then repeated after a predetermined period of time. Further information regarding this process can be found in PCT Patent Specification No. WO 03/025862. 
         [0051]    For example, pre-processed first samples may be subjected to spectral analysis by a spectrum analyser module (not shown) supported by the controller  100 . In this example, the processor  500  carries out a Fast Fourier Transfer (FFT). The FFT of the pre-processed first samples yields a spectrum which reveals much information not only about the operation of the first servo-motor, but also one or more mechanical element coupled directly, or indirectly, to the first servo-motor. In this, and other, examples, a sub-assembly of the packaging machine comprises the one or more mechanical element. 
         [0052]    If required, filters can be used to “clean-up” sampled driver signals so as to facilitate improved accuracy of spectral analysis. 
         [0053]    Following generation of the spectrum for the pre-processed first samples, the second database  516  is interrogated (step to obtain information relating to one or more relevant parameter extractable from the spectrum by analysis thereof, and corresponding to one or more known causes of abnormal operation of the packaging machine. In this example, for a given sub-assembly associated with the spectrum, dry friction, oily friction, sprocket engagement frequency, and lug frequency are some of the pre-programmed parameters for which values corresponding to these parameters can be ascertained from the spectrum. Consequently, for a given parameter such as dry friction, the second database  516  comprises a number of statistical parameters for each characteristic. Once the relevant pre-programmed parameters along with the identity of one or more frequency characteristic of each relevant parameter have been obtained from the second database  516 , the amplitude(s) at the identified one or more frequency is/are determined from the spectrum and stored in the second database  516 . 
         [0054]    The pre-programmed statistical parameters are originally determined by performing statistical analysis on a number of samples of the characteristic parameter obtained from one or more machines of the same or similar type that are known to be operating correctly. For example, although a machine may be similar, it may have characteristics that cannot be applied directly to the machine under test. However, by scaling the characteristic parameters for all the similar (or same) types of machines, a set of samples can be obtained that can be used to provide statistical data that is normalised. The normalised data is used to determine statistical parameters, for example mean μ and variance □ 2  or standard deviation □. This analysis assumes that the characteristic parameter for a correctly operating machine will lie within a standard “Bell-shaped” distribution (a Normal distribution curve) given by the following equation: 
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         [0055]    The statistical parameters determined from the analysis are then used to determine whether the measured characteristic parameter sensed (and, possibly, pre-processed) from the machine being monitored or tested, lies within the Normal distribution and how far from the distribution curve it lies. This information can then be compared to threshold levels to determine what kind of alarm should be triggered, for example, whether it is only the display for the operator to indicate that the machine element is beginning to diverge from the average, but maintenance can wait, or if, at the other extreme, the element is so far from the average that it is expected that it could fail at any time, and therefore the machine is automatically shut down before the part fails and, potentially causes damage. 
         [0056]    This is best shown in  FIG. 5 , where the pre-programmed statistical parameters and the sensed (and, possibly pre-processed) characteristic parameters are read (step  700 ) by the processor  500  from the storage device  512 . The statistical parameters are then used to determine whether the sensed characteristic parameters fall within the Normal distribution curve for that machine or module and by how far they vary from the average (step  702 ). The magnitude of that variation is then compared (step  704 ) to preset threshold levels, also pre-stored in the storage device  512 , and the controller then generates any one of several different alarm options, depending on which threshold level is exceeded. 
         [0057]    Upon detection of abnormal operation, information relating to the abnormal operation of the packaging machine  401  can be communicated to a service engineer, for example, via the display  504 . Additionally, or alternatively, the supervising computer  420  can issue an instruction to the PLC  418  to activate the auto-correction device, such as the micro-sprayers attached to the packaging machine  401  in order to provide corrective maintenance to the one or more mechanical element to cause the packaging machine  401  to revert to a state of normal operation. Other corrective, or preventative, measures already described above in previous examples can also be employed. For example, an escalated alarm may advise the operator that there is a fault, so that the operator can stop the machine as soon as possible, and the highest level of alarm may mean that the controller automatically stops the operation of the machine immediately. It will, of course, be apparent that other desired alarm generated actions, may be used, if desired. 
         [0058]    Alternative embodiments of the invention can be implemented as a computer program product for use with a computer system, the computer program product being, for example, a series of computer instructions stored on a tangible data recording medium, such as a diskette, CD-ROM, ROM, or fixed disk, or embodied in a computer data signal, the signal being transmitted over a tangible medium or a wireless medium, for example microwave or infrared. The series of computer instructions can constitute all or part of the functionality described above, and can also be stored in any memory device, volatile or non-volatile, such as semiconductor, magnetic, optical or other memory device.