Rolling contact silicon nitride bearing technology : a review of recent research

A system (100), method and program product for monitoring a complex signal for ultrasensitive detection of state changes, or for signature recognition and classification is provided. A complex signal is decomposed periodically for empirical modeling (106). Wavelet analysis, frequency band filtering or other methods (102) may be used to decompose the complex signal into components. A library (104) of signature data may be referenced for selection of a recognized signature in the decomposed complex signal. The recognized signature may indicate data being carried in the complex signal. Estimated signal data may be generated for determination of an operational state of a monitored process or machine using a statistical hypothesis test (112) with reference to the decomposed input signal.

Complex signal decomposition and modeling

The present invention involves a method of operating a motive body, where a target path is identified. The motive body is commanded to follow the target path from an initial position. The first actual position is sensed of the motive body after the commanding step. A first target subpath is calculated from the first actual position to correct for predictive errors in movement of the motive body. The actual position of the motive body is determined along each of a plurality of axes, and a multi-dimensional position error between the actual position of the motive body in each of the axes and a multi-dimensional reference trajectory is calculated. A multi-dimensional position error signal dependent upon the calculating step is determined. At least one of a speed and a direction of the motive body is established dependent upon the multi-dimensional position error signal.

Method of tolerance-based trajectory planning

A monitoring system for determining the condition of an object includes an empirical model with reference data that indicates the normal operational state of the object, and input pattern arrays. Each input pattern array has a plurality of time-ordered input vectors, while each input vector has input values representing a plurality of parameters indicating the current condition of the object. The empirical model generates estimate values based on a calculation that uses an input pattern array and the reference data to determine a similarity measure between the input values and reference data. A differencing module compares the estimates values to the corresponding input values so that resulting values from the comparison can be used to determine the condition of the object.

Monitoring Method Using Kernel Regression Modeling With Pattern Sequences

A method and apparatus are provided for diagnosing faults in a monitored system that is monitored by sensors. An empirical model is generated for a targeted component of the monitored system. The empirical model is trained with an historical data source that contains example observations of the sensors. Substantially real-time estimates are generated based on instrumented data corresponding to the targeted component. The substantially real-time estimates are compared and differenced with instrumented readings from the sensors to provide residual values. The residual values are analyzed to detect the faults and determine a location of the faults in the monitored system.

Kernel-based method for detecting boiler tube leaks

An intelligent machining system employs a neural network for calculating machining conditions on the basis of attribute data or a workpiece and a grinding machine. The system comprises a reference machining condition calculating unit, a neural network which receives the attribute data and provides corrections, and a correcting unit for correcting the reference machining conditions by using the corrections. Corrections which cannot be determined by means of empirical expressions or theoretical expressions are determined appropriately by the neural network previously learned. The system determines corrections for the machining conditions on the basis of machining errors decided by a neural network. The system also detects time-series machining phenomena by sensors, processes the output detection signals of the sensors by a neural network to obtain machining circumstance data. The feed rate of the tool is controlled by a fuzzy inference on the basis of the machining circumstance data.

Intelligent machining system

Disclosed is a processing apparatus capable of optimizing its rule base comprising a plurality of rules having an antecedent and a consequent each. The antecedent contains input conditions of machining, and the consequent describes the result of machining. The antecedents are applied to input condition data. The consequents of the applied rules yield machining result data that are evaluated for rule fitness. When the rule base is updated, some rules are deleted and some preserved depending on their degrees of fitness. Between given two rules having the same consequent, any number of corresponding conditional elements in their antecedents are exchanged so as to generate new rules. The process is repeated until the rule base is optimized. With the antecedents of rules applied to input condition data and with the consequents of the applied rules yielding machining result data, those data may be modified according to modification request data. The consequents of rules are applied to the modified machining data, and the antecedents of these applied rules furnish corrective input condition data. This backward application of rules is an easy way to obtain corrective input condition data.

Rule fitness determination, rule removal, and new rule generation

PURPOSE: To make it unnecessary to require techinical expertism such as operator's experience and intuition by providing this intelligent working system with a correction means for correcting a working condition determined by an automatic working condition determining means in accordance with an operator's instruction and obtaining a corrected working condition. CONSTITUTION: Data relating to a work such as quality, surface roughness and dimension accuracy are stored in an attribute data storing means A01. The automatic working condition determining means A02 determines a working condition considered as an optimum one based upon the data stored in the means A01, and when the operator is displeased at the working condition, the condition is corrected by the correcting means A03. A working control means A04 controls the working of the work based upon the corrected working condition, a working diagnosing means A05 diagnoses the result of the working, and when the correction of the working condition is necessary as the result of diagnosis, the working condition is furthermote corrected. When the working condition is corrected, a learning means A06 is validated and the corrected working condition is obtained from the same input as the uncorrected working condition. Consequently, worker's technical skillness is not required. COPYRIGHT: (C)1992,JPO&Japio

Intelligent working system

A device for detecting a position of an edge of a cutting tool includes a reference plate positioned to form a slit between the edge of the cutting tool and the reference plate. The slit extends in a direction transverse to the longitudinal direction of the cutting tool. A laser light source is provided to irradiate a laser light to the slit. A detecting device detects a diffraction pattern created through diffraction of the laser light by the slit. A processing device is provided to determine the position of the edge of the cutting tool based on an output signal of the detecting device.

Device and method for detecting position of edge of cutting tool

This paper presents a high speed non-circular generating mechanism for a piston-head of a reciprocating engine. An ordinary piston-head has the complicated cross-section whose profile is ellipse with harmonic components, so that it is difficult to machine the workpiece at high speed by the copying lathe which is used generally. In order to solve this problem, an NC-tool positioning mechanism using hybrid system which has both the VCM's feature, long stroke, and the PZT's feature, fast response was developed. And a periodic learning control method was also developed which corrects the NC data in the frequency domain. The PZT is mounted coaxially on the top of the externally pressurized slideway which guides the VCM, and the sum of these two actuators' displacement becomes cutting tool's displacement. On account of sharing the harmonic components of profile, i. e. lower components to the VCM, higher components to the PZT, the piston-head can be machined more than three times as fast as the conventional system at the speed of 3 000 min-1, resulted in the accuracy of ±3μm tolerance.

https://www.jstage.jst.go.jp/article/jjspe1986/58/9/58_9_1503/_pdf

Kunihiko Unno

Papers

Intelligent machining system

Rule fitness determination, rule removal, and new rule generation

Intelligent working system

Device and method for detecting position of edge of cutting tool

Development of a Non-circular High Speed Generating Mechanism by Hybrid System with VCM and PZT.