Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Mechanical measurement system
Reexamination Certificate
1997-07-28
2001-10-23
Wachsman, Hal (Department: 2857)
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Mechanical measurement system
C702S060000, C700S174000, C700S175000
Reexamination Certificate
active
06308138
ABSTRACT:
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 modem 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.
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 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 previous invention was disclosed and claimed in U.S. Pat. No. 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 generated 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 crossings 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 minimum 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. When 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.
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Feature Extraction and Assessment Using Wavelet Packets for Monitoring of MachIning Processes by Dr. Ya Wu and R. Du, (No Date).
Jones Joel W.
Wu Ya
Reising Ethington, Barnes, Kisselle, Learman & McCulloch, P.C.
Tri-Way Machine Ltd.
Wachsman Hal
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