Tool condition monitoring system

A tool monitoring system monitors the condition of an electrically powered tool performing a cyclical operation. The tool monitoring system operates generally in two modes: learning mode and monitor 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 and 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 determine 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 application claims the benefit of U.S. Provisional application Ser. 
No. 60/001,926 filed Apr. 8, 1995, which is now abandoned. 
BACKGROUND OF THE INVENTION 
The present invention relates to a tool monitoring system for monitoring 
the condition of an electric motor driven tool performing a cyclical 
operation. 
Tool condition monitoring is one of the major concerns in modern machining 
operations, especially in machining 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. 
SUMMARY OF THE INVENTION 
The present invention provides a real-time tool monitoring system for 
monitoring the condition of an electric motor driven tool performing a 
cyclical operation. 
In the inventive tool monitoring system, an accurate dynamic threshold is 
generated by monitoring the actual power consumption of a machine tool of 
the selected tool type while the machine tool performs a plurality of 
machining cycles. The power consumption signal of the machine tool is 
decomposed into its time-frequency components and reconstructed based upon 
certain selected components in order to reduce the effects of noise. In 
the present invention it is also recognized that the power consumption 
signal of a machine tool in normal condition will include a number of 
spikes in each machine tool cycle. Accordingly, the tool monitoring system 
monitors the number of times the power consumption signal crosses a 
selected threshold, rather than indicating an alarm after a single 
crossing of a larger threshold. 
The tool monitoring system of the present invention generally operates in 
two modes: learning mode and monitoring mode. In the learning mode, the 
tool monitoring system measures the power consumption signals of a certain 
number of samples (say 20, 50 or 100 samples) of the selected tool type as 
the tool performs the selected cyclical task. The tool is known to be a 
new tool or in a normal condition. The tool monitoring system then uses a 
mathematical technique known as wavelet packet transform to break the 
power consumption signal into components. The system selects the 
components that contain the bulk of the information about the overall 
signal, when using wavelet transforms, the selected components are the 
"feature wavelet packets." The selected components contain sufficient 
information about the original signal but not unnecessary or unwanted 
components such as noise. The feature wavelet packets of the power 
consumption signals of the learning cycle are then calculated. 
The tool monitoring system then uses these feature wavelet packet to 
develop thresholds. In one method, the system calculates the inverse 
wavelet packet transform of the feature wavelet packets to reconstruct the 
power consumption signal of each learning sample. A power threshold, 
having an upper limit and a lower limit, is then generated based upon the 
reconstructed power consumption signals of the learning cycle. The power 
threshold is a function of time relative to the machine tool cycle. The 
power threshold is not the extremes of the signal, but rather some 
statistic functions of the signals. The signals of learning cycle will 
cross the threshold some number of times. The tool monitoring system then 
counts the number of crossings of the power threshold by the learning 
cycle power consumption signals and calculates their statistical 
properties. 
In monitoring mode, the tool monitoring system continuously measures the 
power consumption signal of the tool performing the cyclical task. The 
tool monitoring system counts the number of crossings of the power 
threshold by the monitoring power consumption signal and compares the 
number of crossings to the statistical data regarding the number of 
crossings gathered from the learning mode. The tool monitoring system 
generates an alarm when the number of crossings of the power threshold by 
the power consumption signal increases to some predetermined amount 
relative to the number of crossings experienced in the learning mode.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
FIG. 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 tool monitoring system 10 is shown 
monitoring a machine tool 18 having an electric motor 20 driving a tool 
22. For purposes of illustration, the machine tool 18 is shown machining a 
series of workpieces 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 
workpiece 24, and then the conveyor system 26 positions another workpiece 
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. 
FIG. 2 shows one cycle of the power consumption signal 28 of the machine 
tool 18 of FIG. 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 beginning of the cycle, the tool 22 is not engaging the 
workpiece 24 and the power consumption signal 28 is at idling power 30. 
During the initial engagement 32 of the tool 22 with the workpiece 24, the 
power consumption signal 28 rises. When the tool 22 is fully engaged in 
the workpiece 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 
workpiece 24. Due to this fluctuation, it has been difficult to use a 
power signal to accurately predict tool condition. A high "spike" in the 
signal from an unworn tool might be sometimes interpreted as a worn tool. 
The present invention overcomes this problem. After completion of 
machining the tool is withdrawn. During withdrawal 36 the power 
consumption signal 28 decreases steadily and finally returns to idling 
power 38. 
The inventive tool monitoring system 10 is generally based upon the 
observation that the machine tool 18 will consume more power to perform 
the same work when it reaches a worn condition. 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. 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 signals an alarm when 
the tool monitoring system 10 detects that the tool 22 is worn. The 
determination is made by comparing the signal to the expected learning 
cycle, signal ranges, or thresholds. Since the thresholds are developed by 
samples, they are more accurate than prior art "selected" thresholds. 
FIG. 3 shows a flow chart for the learning mode 40 of the tool monitoring 
system 10 of FIG. 1. In learning mode 40, numerous learning cycles of a 
plurality of tool 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 below. 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 statistic function of the signal. The learning 
cycle signals will occasionally exceed 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 each power consumption signal. The crossings of the lower 
limit of the power threshold can be counted separately from the crossings 
of the upper limit of the power threshold, or as a separate number. 
In 56, the CPU 16 calculates the statistic properties of crossings of the 
power consumption signals of the learning cycles. If the upper limit 
crossings are counted separately from lower limit crossings, the two means 
would also be calculated separately. The means and variances will be 
compared to the monitored signals. Since the system compares expected 
numbers of crossings, rather than looking for a single crossing, the 
occurrence of a few "spikes" in a monitored signal will not lead to a 
false indication that a tool is worn. 
As mentioned, in the preferred embodiment, the power consumption signal 28 
is broken into its components using wavelet packet transforms. Wavelet 
transform is a signal processing technique. The wavelet transform 
decomposes a signal into various components at different time scales and 
frequency bands, all of which form a surface in time-frequency plane. Both 
the time scale and the length of the frequency band can be changed, hence 
the characteristics of a signal can be magnified upon different 
resolutions. 
The wavelet transform is more fully discussed in "Feature Extraction and 
Assessment Using Wavelet Packets for Monitoring of Machining Processes" by 
Ya Wu and R. Du, which is hereby incorporated by reference. In the 
preferred embodiment of the present invention, the CPU 16 calculates a 
discrete wavelet transform, specifically the wavelet packet transform. The 
wavelet transform is defined as follows: 
Where the wavelet bases is: 
##EQU1## 
The wavelet transform can be considered as signal decomposition. It 
decomposes a signal f(t) into a family of wavelet bases, and the weighting 
coefficients, W.sub.s [f(t)], represent the amplitudes at given location t 
and frequency s. The wavelet transform is a time-frequency function which 
describes the information of fit) in various time windows and frequency 
bands. It forms a three-dimensional figure against time-frequency plane. 
As a result, wavelet transform is capable of capturing non-stationary 
signals such as frequency variation and magnitude undulation. 
The properties of wavelet transforms are determined by wavelet base 
functions. A number of wavelet base functions have been developed. When a 
wavelet base function, .PSI.(.multidot.), is specified, its family, 
.psi..sub.s.tau. (t), is called the wavelet bases. 
For digital signals, discrete wavelet transforms can be used. In discrete 
wavelet transforms the frequency parameter, s, is taken as an integer 
power of two, i.e., s=2.sup.j, j=1, 2, . . . ; and the time parameter, t, 
is taken as a series of integer k (i.e. t.fwdarw.k=1,2 . . . ); that is: 
##EQU2## 
One of the most commonly used discrete wavelet transform is binary 
orthogonal wavelet transform. Let A.sub.j [.multidot.] and D.sub.j 
[.multidot.] be a pair of operators. At jth resolution, A.sub.j [f(t)] is 
an approximation of the signal f(t) and D.sub.j [f(t)] represents the 
information loss, or the detail signal [5]. It has been verified [4]: 
EQU A.sub.j [f(t)]=f(t)*.phi..sub.j (t) 
EQU D.sub.j [f(t)]=f(t)*.PSI..sub.j (t) 
where, .phi..sub.j (t) is a smooth scaling orthogonal bases, .PSI..sub.j 
(t) is an orthogonal wavelet bases, and "*" denotes convolution. 
Furthermore, .phi..sub.j (t) and .PSI..sub.j (t) are correlated through a 
pair of quadrature mirror filters h(t) and g(t) defined below: 
EQU .phi..sub.j (t)=h(t)*.phi..sub.j-1 (t) 
EQU .PSI..sub.j (t)=g(t)*.PSI..sub.j-1 (t) 
In one embodiment of the present invention, a pair of 4th-order filters are 
used as defined below: 
______________________________________ 
t = 0 t = 1 t = 2 t = 3 
______________________________________ 
h(t) 0.48296 0.83692 0.22414 
-0.12941 
g(t) 0.12941 0.22414 -0.83652 
0.48296 
______________________________________ 
From the above equations, the discrete binary wavelet transform is then 
obtained: 
EQU A.sub.j [f(t)]=h(t)*A.sub.j-1 [f(t)] 
EQU D.sub.j [f(t)]=g(t)*A.sub.j-1 [f(t)] 
or 
EQU A.sub.0 [f(t)]=f(t) 
EQU A.sub.j [f(t)]=.SIGMA..sub.k h(k-2t)A.sub.j-1 [f(t)] 
EQU D.sub.j [f(t)]=.SIGMA..sub.k g(k-2t)A.sub.j-1 [f(t)] 
where, t=1, 2, . . . , N, j=1, 2, . . . , J, and J=log.sub.2 N. 
Since the wavelet transform is a complete representation of the signal, the 
original signal f(t) can be reconstructed by means of inverse wavelet 
transform or the reconstruction formula below: 
EQU A.sub.j [f(t)]=2{.SIGMA..sub.k h(k-2t)A.sub.j+1 [f(t)]+.SIGMA..sub.k 
g(k-2t)*D.sub.j+1 [f(t)]} 
where, j=J-1, J-2, . . . , 1., 0. 
Let operators H and G be the convolution sum defined below: 
EQU H=.SIGMA..sub.k h(k-2t) 
EQU G=.SIGMA..sub.k g(k-2t) 
Then, 
EQU A.sub.j [f(t)]=HA.sub.j-1 [f(t)] 
EQU D.sub.j [f(t)]=GA.sub.j-1 [f(t)] 
It is seen that the binary wavelet transforms uses H and G only on the 
approximation A.sub.j-1 [f(t)] but not on the detail signal D.sub.j-1 
[f(t)]. If the operators H and G are applied on both A.sub.j-1 [f(t)] and 
D.sub.j-1 [f(t)], then, it results in the wavelet packet transform. The 
wavelet packet transformation can be computed by the recursive algorithm 
below: 
EQU P.sub.0.sup.1 (t)=f(t) 
EQU P.sub.j.sup.2i-1 (t)=HP.sub.j-1.sup.i (t) 
EQU P.sub.j.sup.2i =GP.sub.j-1.sup.i (t) 
where 
EQU P.sub.j.sup.i (t) 
is the ith packet on the jth resolution, t=1,2, . . . , 2.sup.J-j, i=1,2, . 
. . , 2.sup.j, j=1, 2, . . . J, J=log.sub.2 N. 
The original signal f(t) can be reconstructed by the inverse wavelet 
transform below: 
EQU P.sub.j.sup.i (t)=2[HP.sub.j+1.sup.2i-1 (t)+GP.sub.j+1.sup.2i (t)] 
where, j=J-1, J-2, . . . , 1, 0; i=2.sup.j, 2.sup.j-1, . . . , 2, 1, and 
the operators H and G are the conjugate of H and G: 
EQU H=.SIGMA..sub.k h(t-2k) 
EQU G=.SIGMA..sub.k g(t-2k) 
Returning to the learning cycle as shown in FIG. 3, at step 46, the CPU 16 
calculates all of the wavelet packets to select the feature wavelet 
packages. As will be shown wavelet packet transforms result in breaking a 
complex signal into a number of components, with only a few of the 
components carrying the bulk of the signal information. 
FIG. 4 shows the wavelet transform 60 of the power consumption signal 28 of 
FIG. 2. As can be seen from the figure, the wavelet transform 60 
calculated to a first resolution 62 is decomposed into a first packet 64 
and a second packet 66. The first packet includes most of the information, 
while the second packet generally includes background noise. At a second 
resolution 68, the wavelet transform 60 comprises four packets. At each 
resolution, the wavelet packets contain the complete information for the 
original signal. In one embodiment, the CPU 16 calculates the wavelet 
transform 60 to at least its fifth resolution 70, which comprises 32 
packets. From this calculation, one can determine that at the fifth 
resolution, the first packet, P.sub.5.sup.1 (t) represents the trapezoid 
trend and the 11th packet, P.sub.5.sup.11 (t) represents the dynamic wave. 
These two wavelet packets may then be selected as the feature wavelet 
packets. The feature wavelet packets can be selected by taking the wavelet 
packets containing the most information, or the most energy. Increasing 
the number of wavelet packets selected increases the accuracy to which the 
components represent the original signal, however, the more packets that 
are selected the more complicated the calculation becomes. The number of 
wavelet packets can be increased until the desired accuracy is achieved. 
In step 50, 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 of one of the learning cycles, shown in FIG. 5, 
therefore comprises the principal components of the power consumption 
signal 28, without the unwanted components such as various noises. 
In step 52, the CPU 16 generates a power threshold 78, as shown in FIG. 6, 
which is based upon statistical properties from the reconstructed power 
consumption signals 76 from the learning cycles. The mean and standard 
deviation of the reconstructed power signals 76 are calculated at each 
time segment relative to the machine tool cycle. Therefore, both the mean 
power consumption signal and the standard deviation are functions of time 
over one machine tool cycle. The power threshold includes an tapper limit 
80 and a lower limit 82, which are both functions of time over the machine 
tool cycle. The thresholds are selected to be some function of the mean 
and standard deviation of the learning cycles signals. In this embodiment 
upper limit 80 and lower limit 82 are preferably calculated as plus and 
minus a certain number of standard deviations from the mean of the 
reconstructed power consumption signals 76 of the learning cycles. 
In step 54, the CPU 16 compares the power consumption signal 76 from the 
learning cycles with the power threshold 78. One of the power consumption 
signals 76 from the learning cycles is shown in FIG. 6. In practice, the 
power consumption signals from numerous learning cycles would be compared 
with the power threshold 78. Notably, the signal in FIG. 6 is shown 
crossing the thresholds. The CPU 16 counts the number of crossings by the 
power consumption signals 28 of the learning cycles of the upper limit 80 
and lower limit 82 of the power threshold 78. The CPU 16 then determines 
the mean number of crossings by the learning cycle signals in step 56. The 
upper limit 80 crossings can be counted separately from the lower limit 
crossings 82, or a composite number. 
After creating a power threshold 78 and statistical data from crossings of 
the power threshold in learning mode 40, the tool monitoring system 10 
enters the monitoring mode 84, shown in FIG. 7. In monitor mode 84, the 
tool monitoring system 10 is again connected to a machine tool 18 of the 
selected tool type as shown in FIG. 1. Preferably, the same CPU 16 is used 
in both the learning mode 40 and monitor mode 84, 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 workstation for the learning mode will 
insure that any individual characteristics of the motor, tool mounts, etc. 
will be accounted for in the thresholds. 
In monitor mode 84, as shown in FIG. 7, the CPU 16 keeps a counter, 
"Crossings", which is initially set to zero in step 86. The CPU 16 
monitors the power consumption signal 28 of the machine tool 18 while the 
machine tool 18 performs its repetitive cyclical machining operations in 
88. 
In step 91, the CPU 16 compares the power consumption signal of the machine 
tool 18 to a catastrophic threshold. The catastrophic threshold is chosen 
to be much larger than the power threshold 78. It can be based upon the 
data gathered in the learning mode 40 or can be determined beforehand. The 
catastrophic threshold is selected to be so high, as to only be met when 
there is a severe failure. If the power consumption signal 28 crosses the 
catastrophic threshold at any time, the CPU 16 signals an alarm in step 92 
which immediately ceases the machining operation and disengages the motor 
20 and spindle 22. 
In step 94, the CPU 16 monitors whether the power consumption signal 28 
fails to rise above the idling power 30 in the time segments of the cycle 
corresponding to the initial engagement of the tool with the workpiece 24. 
This indicates that the workpiece 24 is missing or that the tool 22 is 
broken. If so, the CPU 16 signals an alarm 92. This threshold can be based 
upon the data gathered in the learning mode 40 or can be determined 
beforehand. 
The CPU 16 then determines whether the power consumption signal 28 crosses 
the power threshold 78 in 96. If the power consumption signal 28 crosses 
the power threshold 78, the CPU increments the counter, Crossings, in step 
98. In step 100, during a cutting cycle, as soon as the counter indicates 
that the number of crossings by the power consumption signal 28 of the 
power threshold 78 is more than of crossings calculated in the learning 
mode, the CPU 16 indicates an alarm 92. Since the crossings are counted 
during a cutting cycle, the number of crossings can be compared to 
statistical data from the learning mode before the end of the cycle, i.e. 
the number of crossings can be compared for selected fractions of the 
cutting cycle. 
In 102, if the CPU 16 detects that the machine tool 18 has not completed a 
cycle, the CPU 16 return to step 88 to monitor the next time segment of 
the power consumption signal. If the end of a cycle is detected, then the 
number of crossings, which is stored in "Crossings", is stored with a 
predetermined number of previous numbers of crossings. 
In step 106, the CPU evaluates the trend of the number of crossings by the 
power consumption signal 28. If the number of crossings is increasing 
steadily over the previous predetermined number of cycles, this would 
indicate that the machine tool 18 is worn and heading towards failure. The 
CPU 16 may indicate an alarm 92. 
If the end of a cycle is detected in 102, and the trend of crossings does 
not indicate tool wear in 106, the counter, Crossings, is reset in step 86 
and the tool monitoring system 10 monitors the next cycle. 
Note that all of the steps above require only one addition and comparison, 
therefore they can be performed within the time interval of two monitoring 
sample points, so that it can be used for real-time monitoring. Moreover, 
the present tool monitoring system 10 accurately predicts tool failure by 
selecting a threshold and dealing with the effects of various noises in 
several ways. The tool monitoring system monitors only selected components 
of the power consumption signal to eliminate the effects of unwanted 
noise. In recognizing that the power consumption signal of a tool in 
normal condition will have a number of spikes, the tool monitoring system 
monitors the number of crossings of the chosen power threshold, rather 
than indicating an alarm after a single crossing of a larger power 
threshold. Further, by basing the threshold on statistical data from the 
power consumption signal from learning cycles, the power threshold is an 
accurate function of time over the machining cycle. 
It should be understood that this invention can be broadly utilized in a 
number of distinct fashions. As one example, the learning cycle could be 
performed with the first several cycles of each new tool. Thus, the system 
could be placed in a new tool, and can recalculate its thresholds with the 
first several cycles, which will be known to be operations on a new tool. 
With such a system, there is also the alternative for including in the 
memory of the controller an estimate of what the thresholds typically 
arrived at for the particular type of tool. Thus, when the tool begins to 
set its own thresholds based on its actual learning cycle, it will be 
modified from this predicted threshold. Moreover, these predicted 
thresholds are utilized during the first several cycles to predict 
catastrophic failure, as described above. 
With regard to the actual monitoring of the tool during its cyclical 
operation, it is anticipated that the actual monitored signals will not 
need to be broken into components. That is, the actual raw signals can 
simply be compared to the power thresholds in a real-time fashion. This 
greatly simplifies the calculation and required times to determine when a 
tool is worn. In such a system, some simple adjustment of the thresholds 
as calculated from the learning cycles may be necessary to include the 
effect of the removed information from the learning cycles. This 
adjustment may be as simple as adding some fixed amount to the thresholds 
to account for that which was removed to create the thresholds. 
Alternatively, it may also be preferable to take only components of the 
signal that is being monitored and compare those components to the 
thresholds. That is, the actual monitoring of the tool may utilize the 
same method steps that were used to create the thresholds in its learning 
cycle. 
In accordance with the provisions of the patent statutes, the present 
invention has been described in what is considered to represent its 
preferred embodiment. However, it should be noted that the invention can 
be practiced otherwise than as specifically illustrated and described 
without departing from its spirit or scope.