Siemens Corporate Research, Inc.

Thomas Petsche

Charles Garrett

Stephen J Hanson

Angelo R Marcantonio

Nugroho Iwan Santoso

A computer-based system and method for ascertaining anomalies in an electric motor, includes computing and processing a set of Fast Fourier Transforms (FFT's) of the supply current waveforms for a motor known to be in a normal condition to create a feature set of input vectors. The input vector assignments for all possible cluster groupings 1 through n are derived using Ward's method. The method includes computing the Approximate Weight of Evidence (AWE) for each cluster grouping then selecting that count associated with a maximum AWE and designating this count as s. For each cluster in each grouping 1 through s, the method finds the member vector farthest from the cluster's centroid and defines this as the cluster's radius. A single input sample for a motor under supervision is read in, following by computing and processing an FFT based on the input sample for a newly generated feature vector and then checking whether the newly generated feature vector is inside any of the clusters 1 through s as defined for each cluster by its respective radius, and if not, outputting a warning signal.

Method and system for ascertaining anomalies in electric motors

A method for blade temp estimation in a steam turbine utilizes measurement values including pressure and temperature at locations other than directly at the blades, principally at the input and output stages. Initially, blade temperature is simulated by using a water/steam cycle analysis program as well as by directed experiments. An artificial neural network (ANN) is trained by presenting the measurement values and the blade temp values. In a hybrid approach, 5 measured values are utilized. A subset of 4 parameter values is used for training the ANN and another subset of 3 values is used for performing a calculation for another intermediate parameter. Using the intermediate parameter and one of the 5 measured values, a blade temperature is calculated.

Method for blade temperature estimation in a steam turbine

A method for detecting a departure from normal operation of an electric motor comprises the steps of modelling a set of normal current measurements and a set of operational current measurements for the motor being monitored. The modelling is carried out by a neural network auto-associator which is trained to reproduce its inputs on its output. A potential failure is indicated whenever the set of normal current measurements and the set of operational current measurements differ by more than a predetermined criterion.

Method for monitoring an electric motor and detecting a departure from

A method for detecting a departure from normal operation of an electric motor comprises obtaining a set of normal current measurements for a motor being monitored; forming clusters of the normal current measurements; training a neural network auto-associator using the set of normal current measurements; making current measurements for the motor in operation; comparing the input and output of the auto-associator; and indicating abnormal operation whenever the current measurements deviate more than a predetermined amount from the normal current measurements. The method models a set of normal current measurements for the motor being monitored, and indicates a potential failure whenever measurements from the motor deviate significantly from a model. The model takes the form of an neural network auto-associator which is 'trained'--using clusters of current measurements collected while the motor is known to be in a normal operating condition--to reproduce the inputs on the output. A new set of FFT's of current measurements are classified as 'good' or 'bad' by first transforming the measurement using a Fast Fourier Transform (FFT) and an internal scaling procedure, and then applying a subset of the transformed measurements as inputs to the neural network auto-associator. A decision is generated based on the difference between the input and output of the network.

Radial basis function neural network autoassociator and method for

A method for detecting a departure from normal operation of an electric motor comprises obtaining a set of normal current measurements for a motor being monitored; training a neural network auto-associator using the set of normal current measurements; making current measurements for the motor in operation; comparing the current measurements with the normal current measurements; and indicating abnormal operation whenever the current measurements deviate more than a predetermined amount from the normal current measurements. The method models a set of normal current measurements for the motor being monitored, and indicates a potential failure whenever measurements from the motor deviate significantly from a model. The model takes the form of an neural network auto-associator which is 'trained'--using current measurements collected while the motor is known to be in a normal operating condition--to reproduce the inputs on the output. A new set of current measurements are classified as 'good' or 'bad' by first transforming the measurement using a Fast Fourier Transform (FFT) and an internal scaling procedure, and then applying a subset of the transformed measurements as inputs to the neural network auto-associator. A decision is generated based on the difference between the input and output of the network.

Neural network auto-associator and method for induction motor monitoring

A self-repairing trellis network includes a plurality of stages (11, 12 and 13) wherein a first set of N processing elements and a second set of M processing elements are situated. The N processing elements are normally active while the M processing elements are designated as spares. The stages are connected together by paths through variable weighing circuits which are legal or illegal. In response to a combination of input bits, an arrangement maps bit groupings onto the stages to turn on selective cells which form nodes coupled by paths. Only certain paths (legal paths) are tolerated in accordance with convolution coding of the input bit combination. If an error is present, an illegal path attempts to form from the legal path which starts a process of error detection and correction wherein a spare cell may be substituted for a failed cell. Since the substitution serves to maintain the redundancy status of the network, subsequent failures will be detected and corrected.

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Neshanic Station, NJ, United States of America

Thomas Petsche