DIAGNOSTIC RULE BASE TOOL CONDITION MONITORING SYSTEM

Abstract: A tool condition monitoring system (10) monitors the power consumption of a tool during performance of a cyclical task. The system monitors (16) a plurality of characteristics of the power consumption and diagnoses the condition of the tool based upon the plurality of characteristics. A rule base of the plurality of characteristics is generated in a learning mode by monitoring a tool of known condition for a plurality of cycles. In monitor mode, the system monitors the power consumption of a tool and diagnoses the condition of the tool by indexing the rule based upon the plurality of characteristics monitored. In addition, a method is disclosed wherein the band may be modified for accommodating tool wear, such that a tool, although dull, may be utilized and monitored.

Description: DIAGNOSTIC RULE BASE TOOL CONDITION MONITORING SYSTEM BACKGROUND OF THE INVENTION The present invention relates to a tool monitoring system for monitoring the condition of an electric motor driven tool rjerforrning a cyclical operation. Tool condition monitoring is one of the major concerns in modern machining operations, especially in macm ' ning operations for mass production. Failure to detect tool failure and wear leads to poor product quality and can even damage machine tools. On the other hand, -a false detection of tool failure or wear may cause an unnecessary interruption of an entire production. Both can result in significant monetary loss. Known tool monitoring systems include systems for "on-line tool condition monitoring." In on-line tool condition monitoring, the tool is monitored for defects after each cut or cycle: These tool monitoring systems typically use optical sensors or laser optical sensors which measure the geometry of the tool after each cut. However, on-line tool condition monitoring can only detect catastrophic failure of a tool after a cut and cannot monitor the gradual wear of a tool or predict the tool's failure. Further, these systems are vulnerable to chips, coolant, and environmental noises. Other known methods for tool condition monitoring attempt to predict tool condition based on various sensor signals such as cutting force, acoustic emission, and vibration. However, sensors for monitoring cutting force are too expensive to use with multiple stations and multiple spindles. Acoustic emission and vibration sensors require additional wiring and are vulnerable to various noises. Some monitoring systems monitor power consumption (or motor current) of the tool. As the tool wears (or if it fails) its power consumption changes. However, the power signals are complicated and the power signals to provide a reliable, accurate indication of it has proven difficult to use. The power signal does contain some "noise" due to factors other than tool condition. Typically, these systems sets a range of signal that a monitored signal should fall within. When the monitored signal is outside this range, a worn tool or failure is indicated. One major problem with monitoring the power consumption of the motor is that occasional spikes are experienced in a machine tool even under normal condition. The spikes can falsely indicate that the tool is worn. However, if the threshold is increased to prevent false signals, a worn tool may go undetected. The inventors of the present invention previously developed a tool monitoring system which operates generally in two modes: learning mode and monitoring mode. In learning mode, the tool monitoring system gathers statistical data on the power consumption of tools of the selected tool type during learning cycles. A power threshold is generated based upon the statistical data. The tool monitoring system then counts the number of crossings by each of the learning cycles of the power threshold a^d generates statistical data regarding the number of crossings. Preferably, the mathematical operation of wavelet packet transform is used to calculate the power threshold. Feature wavelet packets of the power consumption signal of the tool are calculated. The power consumption signal is then reconstructed from the feature wavelet packets and used to deteπriine the power threshold. In monitor mode, the tool monitoring system counts the number of crossings of the power threshold by the power consumption signal of a tool in operation. The tool monitoring system identifies a worn tool when the number of crossings increases to a certain number relative to the crossings by the learning cycles. This previous invention was disclosed and claimed in United States Patent Number 5,587,931. SUMMARY OF THE INVENTION The present invention provides a real time tool monitoring system which continuously monitors a plurality of characteristics of the power consumption of the tool during operation in order to diagnose the condition of the tool and the likely cause of any problem. The tool monitoring system of the present invention monitors a plurality of characteristics of the power consumption of the tool during performance of the cyclical task. The system diagnoses the condition of the tool based upon the plurality of characteristics of the power consumption, including the existence or absence of each of the plurality of characteristics. Preferably, the system is first operated in a "learning mode," in which one or more tools of a known condition are monitored for a plurality of cycles. A plurality of characteristics of the power consumption of the known tools evaluated statistically in order to generate a plurality of threshold values. First, a power threshold having an upper limit and a lower limit is geneicted based upon the average power consumption. The power threshold and average power consumption is a function of time over the cyclical task. During each cycle of the tool, the power consumption will cross the power threshold a plurality of times. Statistical information regarding the number of crossmgs by the power consumption of the upper and lower limits by the known tools is gathered to establish a threshold number of crossings of the upper limit and a threshold number of crossings of the lower limit. Further, extreme high and low values, i.e. "spikes," in the power consumption are also monitored in the known tools and evaluated statistically to generate maximum and iniriirnum permissible values. Again, this threshold is a function of time over the cyclical task. The amount of time that the power consumption stays outside the power threshold, i.e. above the upper limit or below the lower limit, is also monitored statistically to generate a threshold time value. The values gathered in the learning mode are then utilized to generate a diagnostic rule base which includes every possible combination of the plurality of characteristics monitored, i.e. number of crossings, maximum & minimum instantaneous values, and time outside threshold. Further, for each characteristic, there are four possibilities. First, the characteristic may be absent, i.e. the number of crossings has not been exceeded, the maximum and minimum permissible values have not been crossed and the time outside the threshold has not been exceeded. hen the characteristic exists there are three more possibilities: the characteristic is occurring below the lower limit of the power threshold, above the upper limit of the power threshold or both above and below the power threshold. Thus, for the three characteristics monitored in the preferred embodiment, there are sixty four possible combinations which are associated with different tool conditions in the rule base. In addition, certain machine operations may be able to utilize a worn tool. For a tooling operation that does not require the most extreme precision cutting, it may not be desirable to indicate a failed tool as rapidly as it would for other more precise cutting operations. Thus, it is desirable to have the thresholds be able to change during operation of the tool, as the tool experiences wear. For this reason, in one variation of the present invention, the system monitors a plurality of characteristics, and certain combinations of characteristics are identified as relearning codes. If a relearning code is experienced, this is taken as an indication that the tool is experiencing some wear, but still may be capable of further use. When a relearning code is monitored, then several recent samples are utilized to relearn a band. BRIEF DESCRIPTION OF THE DRAWINGS The above, as well as other advantages of the present invention, will become readily apparent to those skilled in the art from the following detailed description of a preferred embodiment when considered in the light of the accompanying drawings in which: Figure 1 illustrates a tool monitoring system according to the present invention, monitoring the power consumption of a machine tool machining a series of work pieces. Figure 2 illustrates the power signal from one cycle of the machine tool as received by the tool monitoring system of Figure 1. Figure 3 is a flow chart of the tool monitoring system of Figure 1 in its learning mode. Figure 4 is a reconstructed power consumption signal of Figure 2, reconstructed from the feature wavelet packets selected from Figure 4. Figure 5 is a power threshold based upon several reconstructed power consumption signals the learning cycles. Figure 6 A is a flow chart of the tool monitoring system of Figure 1 in monitor mode. Figure 6B is a flow chart of a control rule base for the tool monitoring system of Figure in monitor mode. Figure 7 is a chart showing the diagnosis rule base for rules 1-4. Figure 8 is a chart of the diagnosis rule base of Figure 7 for rules 5-8. Figure 9 is a chart of the diagnosis rule base of Figure 7 for rules 9-13. Figure 10 is a chart of the diagnosis rule base of Figure 7 for rules 14-17. Figure 11 is a chart of the diagnosis rule base of Figure 7 for rules 18-21. Figure 12 is a chart of the diagnosis rule base of Figure 7 for rules 22-24. Figure 13 is a chart of the diagnosis rule base of Figure 7 for rules 25-27. Figure 14 is a chart of the diagnosis rule base of Figure 7 for rules 28-30. Figure 15 is a chart of the diagnosis rule base of Figure 7 for rules 31-33. Figure 16 is a chart of the diagnosis rule base of Figure 7 for rules 34-37. Figure 17 is a chart of the diagnosis rule base of Figure 7 for rules 38-41. Figure 18 is a chart of the diagnosis rule base of Figure 7 for rules 42-45. Figure 19 is a chart of the diagnosis rule base of Figure 7 for rules 46-49. Figure 20 is a chart of the diagnosis rule base of Figure 7 for rules 50-53. Figure 21 is a chart of the diagnosis rule base of Figure 7 for rules 54-56. Figure 22 is a chart of the diagnosis rule base of Figure 7 for rules 57-60. Docket No: 60.135-033 Figure 23 is a chart of the diagnosis rule base of Figure 7 for rules 61-64. Figure 24 shows a typical band for a new tool and a dull tool. Figure 25 shows the typical band for the new tool of Figure 24 including the calculated tolerance band and limits. Figure 26 is a flow chart of a variation of the present invention. Figure 27 shows the typical band for the new tool of Figure 24 including the areas calculated to reposition the band. DET ILED DESCRIPTION OF A PREFERRED EMBODIMENT Figure 1 shows a tool monitoring system 10 according to the present invention including a current transducer 12 connected to an analog-to-digital converter 14 and a CPU 16. The CPU 16 is also connected to a plurality of condition indicator lights 17a-c, which are preferably green, yellow and red, respectively. The tool monitoring system 10 is shown momtoring a machine tool 18 having an electric motor 20 driving a tool 22. For purposes of illustration, the machine tool 18 is shown ir chiriing a series of work pieces 24 being moved along a conveyor system 26. As will become apparent, the tool monitoring system 10 of the present invention can be used with any selected tool type using an electric motor and performing a repetitive, cyclical task. In operation, the motor 20 and tool 22 are repeatedly loaded to cut each work piece 24, and then the conveyor system 26 positions another work piece 24 to the machine tool 18. The current transducer 12 continuously indicates the power consumption of the motor 20 by sending a power consumption signal to the analog-to-digital converter 14, which converts the power consumption signal into a format readable by the CPU 16. The analog-to-digital converter 14 sends a digital signal representing the amplitude of the power consumption signal at a series of current time segments. The digitized power consumption signal is stored in the CPU 16 and associated with its particular time segment, relative to the machine tool cycle. Figure 2 shows one cycle of the power consumption signal 28 of the machine tool 18 of Figure 1, as received by the CPU 16. The machining operation is in the form of a cycle starting from tool engagement and ending with tool withdrawal. At the begiiming of the cycle, the tool 22 is not engaging the work piece 24 and the power consumption signal 28 is at idling power 30. During the initial engagement 32 of the tool 22 with the work piece 24, the power consumption signal 28 rises. When the tool 22 is fully engaged in the work piece 24, the power consumption signal 28 reaches full engagement consumption 34. At full engagement 34, the power consumption signal 28 reaches a level and remains relatively unchanged, though there are fluctuations caused by various noise, such as cutting a hard spot in the work piece 24. Due to this fluctuation, it has been difficult to use a power signal to accurately predict tool condition. High "spikes" may occur in the signal occasionally even though the tool is not worn. After completion of macmiiing the tool is withdrawn. During withdrawal 36 the power consumption signal 28 decreases steadily and finally returns to idling power 38. As will be explained in detail below, the tool monitoring system 10 according to the present invention generally operates in two modes: a learning mode and a monitoring mode. In learning mode, the tool monitoring system 10 preferably receives data from several sample cycles of machine tools 18 of the selected tool type. Information related to the power consumption during each cycle run by each machine tool 18 is stored to develop expected signal ranges, or thresholds, and other statistical values. Then in monitoring mode, the tool monitoring system 10 compares the power consumption signal of a machine tool 18 with data gathered in the learning mode and diagnoses the condition of the machine tool 18 for each cycle. The determination is made by comparing the signal to the expected learning cycle, signal ranges, or thresholds and other statistical data gathered from tools of a known condition. Since the thresholds are developed by samples, they are more accurate than prior art "selected" thresholds. Figure 3 shows a flow chart for the learning mode 40 of the tool monitoring system 10 of Figure 1. In learning mode 40, numerous learning cycles of a plurality of tools 22 of the selected tool type are run in 42. The tool 22 is selected to be a new tool or in normal condition. The power consumption signals 28 of the learning cycle are digitized by the analog-to-digital converter 14 and stored in the CPU 16 in 44. The CPU 16 then selects feature components of the power consumption signal 28 in 46. In one preferred embodiment, wavelet transforms are used to break the signal into components, as explained in U.S. Patent No. 5,587,931 entitled "Tool Condition Monitoring System" which is assigned to the assignee of the present invention and which is hereby irr.orporated by reference. In 46, the samples of the learning cycle are decomposed into different time-frequency components. The feature wavelet packets are selected from the components to represent the main information about the original power consumption signal 28, thereby the unwanted components of the power consumption signal 28, i.e. noise are filtered out from the signal. In 50, the CPU 16 reconstructs the power consumption signal 28 of each learning cycle from the selected feature wavelet packets by the inverse of the function used to break the original power signal into components. The reconstructed power consumption signal 28 then contains sufficient information from the original power consumption signal 28, but with reduced noise. Notably, while only some of the learning cycles need be used to select the feature wavelet packets in step 46, preferably all of the learning cycles are used to develop data at step 50. The more cycles utilized, the more accurate the system. In 52, the CPU 16 generates a power threshold based upon statistical data calculated at 50 from the learning cycles. The power threshold is a function of time over the machine tool cycle and includes an upper limit and a lower limit. The upper and lower limits are not the extremes of the signal, but rather some statistical function of the signal, preferably the average power consumption plus and minus five standard deviations, respectively. The learning cycle signals are expected to occasionally fall outside these thresholds. In 54, the CPU 16 compares the power threshold to the power consumption signals of the learning cycles. The CPU 16 compares each power consumption signal to the power threshold at each time segment and counts the number of crossings by e£-Λ power consumption signal. The crossings of the lower limit of the power threshold are preferably counted separately from the crossings of the upper limit of the power threshold. In 56, the CPU 16 calculates the statistical properties of crossings of the power consumption signals of the learning cycles. If the upper limit crossings are counted separately from lower limit crossings, two means & two standard deviations would also be calculated separately. In step 57, the CPU 16 calculates the statistical properties of occasional extreme high and low values of the power consumption. Preferably, an instantaneous maximum value is set at the average maximum value plus five standard deviations and an instantaneous minimum value is set at the average mimmum value iriinus five standard deviations. In step 58, the CPU 16 calculates the statistical properties of the amounts of time that the power consumption signals in the learning mode spend outside the power threshold, i.e. above the upper limit or below the lower limit. Preferably, a threshold time for the lower limit is calculated as the average time spent by power consumption signals below the lower limit plus five standard deviations. Similarly, a threshold time for the upper limit is calculated as the average time spent by power consumption signals above the upper limit plus five standard deviations. The reconstructed signal 76 of one of the learning cycles, created in step 50, is shown in Figure 4. The CPU 16 performs the inverse wavelet packet transform on the feature wavelet packets, while setting the other packets to zero. Setting the other packets to zero eliminates noise from the signal. The reconstructed signal 76 therefore comprises the principal components of the power consumption signal 28, without the unwanted components such as various noises. The power threshold 78 created in step 52 is shown in Figure 5. The power threshold 78 is based upon statistical properties from the reconstructed power consumption signals 76 from the learning cycles. The power threshold includes an upper limit 80 and a lower limit 82, which are both functions of time over the machine tool cycle. In this embodiment upper limit 80 and lower limit 82 are preferably calculated as plus and minus five standard deviations, respectively, from the mean of the reconstructed power consumption signals 76 of the learning cycles. Figure 5 also shows one of the power consumption signals 76 from the learning cycles. In learning mode 40, the CPU 16 compares the power consumption signal 76 from the learning cycles with the power threshold 78 to calculate means and standard deviations for number of crossings of the upper and lower limit 80, 82, time spent above the upper limit 80, time spent below the lower limit 82, and maximum and mirώmrrn instantaneous power consumption values. In practice, the power consumption signals from numerous learning cycles would be compared with the power threshold 78. After creating a power threshold 78 and threshold values, the tool monitoring system 10 enters the monitoring mode 90, shown in Figure 6A. In monitor mode 90, the tool monitoring system 10 is again connected to a machine tool 18 of the selected tool type as shown in Figure 1. Preferably, the same CPU 16 is used in both the learning mode 40 and monitor mode 90, however, it is recognized that the power threshold 78 data could be downloaded to a different CPU for the monitor mode. Moreover, it is preferred that the learning mode be performed at the actual work station where the CPU will be monitoring. Using the actual A/D converter 14 for the learning mode signal acquisition will insure that any individual characteristics of the motor, tool mounts, etc. will be accounted for in the thresholds. In step 92 of monitor mode 90, the CPU 16 acquires and digitizes the power consumption signal of a machine tool 18 while the machine tool 18 performs its repetitive cyclical machining operations. In step 96, the power consumption signal is compared to the instantaneous maximum and minimum values calculated in the learning mode. If the power threshold signal falls between the minimum and maximum values, then the value _ma is set to equal 0. If the maximum value is exceeded, then _ma is set to equal 1. If the power consumption signal falls below the miijjmum value, _ma is set to 2. Finally, if the power consumption signal both exceeds the maximum value and falls below the miriimum value, _ma is set to three. In step 97, the CPU 16 monitors the time that the power consumption signal is outside the power threshold, i.e. above the upper power threshold 80 and below the lower threshold 82. In step 98, the CPU 16 counts the number of crossings by the power consumption signal of the upper and lower threshold limits 80, 82. In step 100, the values and numbers from steps 96, 97, 98 are compared to the threshold numbers. The times are compared to the threshold times. If the power consumption signal stays within the band, _cc is set to 0. If the power consumption signal stays below the lower limit of the power threshold 82 longer than the threshold time for the lower limit of the power threshold calculated in the learning mode, then _cc is set to 1. If the power consumption signal stays above the upper limit of the power threshold for time exceeding the threshold time for the upper limit of the power threshold calculated in the learning mode, then _cc is set to 2. If both times are exceeded, _cc is set to 3. Also in step 100, the numbers of crossings are compared to the threshold numbers. If the numbers of crossings is less than the threshold numbers of crossings calculated in the learning mode, then _cr is set to 0. If the number of crossings of the lower power threshold limit exceeds the threshold number of crossings of the lower threshold limit calculated in the learning mode, then _cr is set to 1. If the number of crossings of the upper threshold limit exceeds the threshold number of crossings of the upper power threshold limit, then _cr is set to 2. If both thresholds are exceeded, _cr is set to 3. In step 102, a condition code is calculated from the values for the three characteristics, _cr, _cc and _ma. Preferably, the condition code is a three digit number of the three values, in the form _cr _cc _ma. For example, if _cr=3, _cc=2 and _ma=0, the condition code would be 320. In step 103, the control rule base is indexed based upon a weighing of the condition code values, which will be described in detail below with respect to Figure 6B. Based upon the condition code, the tool condition monitoring system 10 will turn on an appropriate condition indicator light 17a-c, and may send a control command to stop the machine in step 104. In step 106, a different weighing is applied to the condition code utilizing an inference of tetradic and decimal transform. In the preferred embodiment, since each of the characteristics has one of four values, 0-3, each of the characteristics is multiplied by a different multiple of four. The value of _cr is preferably multiplied by 4 2 , or 16. The value of _cc is preferably multiplied by 4 1 , or 4. The value of _ma is preferably multiplied by 4°, or 1. The results are then added to provide a unique decimal number for each possible combination of values for the three characteristics. In the preferred embodiment illustrated here, one is added to the resulting number to eliminate a zero value. It should be apparent that if more characteristics are utilized, higher multiples of four would be utilized. Further, the base number, in this case four, is equal to the number of possible values. The base number could be increased to accommodate more possible values. This technique is illustrated below: _cr*4 2 +_cc*4' + _ma*4° + 1 = Rule index _cr*16 +_cc*4 + _ma*l + 1 Example 1: _cr=0, _cc=0, _ma=0 0*16 + 0*4 + 0*1 +1 = 1 Therefore, diagnosis Rule 1 is indexed. Example 2: _cr= 1 , _cc= 1, _ma=0 1*16 + 1*4 + 0*1 + 1 = 21 Therefore, diagnosis Rule 21 is indexed. The Rule calculated using above method is then used to index a rule base in order to diagnose the condition of the machine tool 22. The tool condition is then indicated in step 108, such by a display on CPU 16 of text indicating the condition of the tool 22 and possible problems: The rule base will be described in detail below with respect to Figures 7-23. Figure 6B shows the control chart 110 for weighing the condition code to index a control rule base. In step 112, the green light 17a (of Figure 1) is turned on and the "stop" command is turned off. In step 114, _ma is tested for a non-zero value. If the instantaneous maximum or minimum values are violated, the green light 17a is immediately turned off, the red light 17c is turned on and a stop command is sent to the machine to cease operation immediately in step 116. If _ma is zero in step 114, then _cr and _cc are tested for zero values. If both are zero, the CPU 16 returns to step 112 and the green light 17a remains on. If _cc and _cr are not both zero, the sum of the squares is compared to 9. If the sum of the squares of _cc and _cr does not exceed 9, the green light is turned off and the yellow light 17b is turned on in step 122. If the sum of the squares of _cc and _cr exceeds 9, which can only occur if one of the values is three and the other is non-zero, the green light 17a is turned off and the red light 17c is turned on in step 124. The CPU 16 returns to step 114 after step 122 or step 124. The diagnosis rule base 130 is shown in Figure 7-23. As described above, the diagnosis rule base is indexed according to the possible values of the monitored characteristics _cr, _cc and _ma. As can be seen in Figure 7, if _cr, _cc and _ma are all 0, Rule 1 is indexed, indicating that the tool is in a normal condition. If _cr equals 0, _cc equals 0, and _ma equals 1, Rule 2 -of rule base 130 is indexed, thereby indicating that either the tool or the work piece is loose and that the process should be stopped. The rerrjaining Rules 3-64 are indexed in a similar manner according the weighing technique described above. The tool condition monitoring system 10 of the present invention thus not only provides a warning of a worn or broken tool, but also diagnoses the present condition of the operation process based upon a plurality of characteristics of the power consumption signal. As shown in Figure 24, a typical tooling cycle will generally have higher current values as the tool becomes worn. A cycle 200 is shown for a good or new tool, and a cycle 202 is shown for a dull tool. The above-described method of this invention might indicate that the tool at cycle 202 is ready for replacement. However, for certain machining operations it may be possible that tool 200 is still capable of further operation. Thus, the present invention adjusts the tolerance band as the tool wears. Simultaneously with adjustment of the tolerance band, the system will still monitor each cycle for certain combinations of conditions which will result in immediate stoppage of the tool. As shown in Figure 25, the cycle 200 for the good or new tool from figure 24 is shown with representative overlays of the calculated upper band tolerance 206 and the calculated lower band tolerance 208. Band 212 is defined between the tolerances 206 and 208 and is user set preferably as plus or minus five standard deviations. Additionally, an overall upper limit 204 and an overall lower limit 210 are displayed to provide a reverence for how the tool is performing. As will be further described below, band 212 provides a high degree of control over the machine operation as a user defined number of previously acceptable cycles are used to calculate the upper 206 and lower 208 band tolerances. The system continues to monitor and compare each cycle of operation to band 212 and band 212 is maintained as the working band until a relearning code is detected. By varying the upper and lower tolerances 206,208 as the tool wears, the present invention determines minute changes in each cycle as normal tool wear is compensated for by the movement of band 212. As shown in Figure 26, a flow chart of one method of the present invention is shown. When a tool is initially changed, samples are taken as with the above embodiments. The position of band 212, that is 206, 208 tolerances, are learned. The system then proceeds to momtor each cycle of operation. The results of that monitoring are detected. The detected results are compared to a code of 0,0,0, or a code which has no problem conditions. If the monitored code is at 0,0,0, then the particular cycle is saved as a relearning sample which will be utilized for relearning as the tool wears. A user defined number of relearning codes continue to be stored to provide for later adjustment of band 212. The most recent 0,0,0 cycle is saved, and when the user defined number is reached, the most recent 0,0,0 cycle replaces the oldest 0,0,0 cycle. By this storage method the most recent and thus the most accurate cycles are used to recalculate the location of band 212. If the code is not 0,0,0, then the next step is to compare the monitored code with a series of relearning codes. As shown in Figure 26, there are a series of twelve codes which may be identified as relearning codes. If one of the relearning codes is monitored, then the upper 206 and lower 208 tolerances of band 212 are reset based upon the previously stored 0,0,0 cycle file samples. As shown in Figure 27 the upper 206 and lower 208 tolerances of band 212 are preferably repositioned based on the areas below the tolerance bands 206,208. The steady state of the rnachiriing operation is used as the zero or baseline to determine the current band position and the repositioned band position. The upper area 214 and the lower area 216 are individually compared with the average of the previously stored 0,0,0 cycle file samples. The upper 206 and lower 208 tolerances of band 212 are then adjusted by the difference between the initial band 212 position and the stored 0,0,0 cycle file samples. As areas are used to calculate the repositioning of the tolerance band 212, overall band limits 204,210 (Figure 25) can be based on the standard deviation of the overall arec to provide an accurate indication of the overall limits of tool usage. These overall limits 204,210 are user defined and are preferably based on previous testing or manufacturer data. The overall limits 204,210 are thus the defined by the acceptable standard deviation difference between the upper 206 and lower 208 tolerances of band 212. Although the overall upper limit 204 and the overall lower limit 210 are shown as horizontal lines in Figure 25, this is for purposes of illustration. By providing horizontal limits the operator can pictorially relate and understand the position and movement of the working band 212 to the outer operational limits 204,210. After calculating the upper area 214 and the lower area 216, if the repositioned working band 212 exceeds the outer operational limits 204,210 the working band is prevented from exceeding the limit. In other words, the working band can never exceed the outer operational limits 204,210. The result of preventing the working band 212 from exceeding the outer operational limits 204,210 is the experience of a greater number of relearning codes. The machine can continue to be operated, however the continued experience of relearning codes is an indicator that a greater number of unacceptable parts are being produced. Thus, it is best that the machine be stopped and use of the current worn tool be discontinued as having exceeded extremes of proper operation. In essence, the present invention looks to see if a relearning code is experienced. If so, then the system proceeds to relearn the working band within the extreme of the upper and lower limits based on recently captured acceptable cycles. In this way, the band position will move with tool wear and indicate minute changes in machine operation. Simultaneously, even if the monitored code is not a relearning code, the system still monitors each cycle for unacceptable conditions. Thus, detection results for each cycle are displayed and corrective measures, such as warning lights will be indicated. However, should an extreme problem occur during a cycle, the system will still be shut down. With this invention, Applicant has found that very precise predictions of when a tool is about to break or fail are achieved. Still, the tool may be used beyond a time when wear is beginnrng, up till the time when its useful life is ended. The foregoing description is exemplary rather than limiting in nature. Obviously, many modifications and variations of the present invention are possible in light of the above teachings. It is, therefore, to be understood that within the scope of the appended claims the invention may be practiced otherwise than as specifically described.

Claims: WHAT IS CLAIMED IS: 1. A method for monitoring the condition of an electric motor powered tool including the steps of: a) monitoring the power consumption of a tool during performance of a cyclical task; b) monitoring a plurality of characteristics of said power consumption during said cyclical task; and c) diagnosing the condition of said tool based upon said monitoring of said plurality of characteristics. 2. The method of Claim 1 wherein a subgroup of possible sets of said plurality of characteristics are defined as relearning codes, and if a relearning code is experienced, a band is recalculated. 3. A method of momtoring a tool operation comprising the steps of 1) running a tool and capturing samples of operational characteristics; 2) analyzing said captured sample characteristics to define a band of expected operation; 3) miming the tool in a production mode; 4) capturing characteristics of operational cycles of said tool during said production made; 5) comparing said characteristics to relearning code characteristics; and 6) resetting said band should said captured characteristics be equal to said relearning characteristics. 4. The method of Claim 3 wherein said step 5) includes the step of diagnosing said condition based upon a combination of said plurality of characteristics. 5. The method as recited in Claim 4 wherein a first subgroup of said captured characteristics include a plurality of codes which identify a proper cycle, and said relearned characteristics utilize a plurality of captured proper cycles to reset said band. 6. The method as recited in Claim 5 wherein upper and lower limits for a band are reset, and if said upper or lower band would exceed extreme limits, then identification is made that said band should be not changed.

Inventors: JONES JOEL W, WU YA, JONES, JOEL, W., WU, YA
Applicants: FEDERAL MOGUL CORP, FEDERAL-MOGUL CORPORATION
Application year: 1999
Main classification: G01N19
IPC codes: G01N3, G05B19, G05B23