A pre-emptive maintenance system for performing maintenance and process assurance on run-critical equipment employs an intrinsic health monitor that includes a number of sensors that are used with an operating equipment or machine to generate a set of intrinsic physical signatures that are products of the primary performance characteristics of the operating equipment These signatures are nonfunctional parametrics that are consistent and reliable indicators of the normal operation of the equipment and statistical norms are set during an initial learning mode using neural processors and the outputs from the sensors Thereafter, the sensor outputs are monitored and analyzed using the statistical norms to predict the probabilities of future failures of the operating equipment By using the intrinsic health monitor an advance in the field of equipment maintenance and process assurance is provided in the approach to maintenance being a pre-emptive procedure

Method and apparatus for performing pre-emptive maintenance on operating equipment

Methods and apparatus for identifying in real time an operating condition of an in-service electrical device which draws a power load by monitoring spectral content of the power signature and associating spectral components with device operating conditions. In the preferred embodiment, motor (12) power line current signature is sampled by data conditioning sampler (18). Preprocessor (30) spectrally separates the current signature into plural frequency domain components having respective frequency and magnitude values. Spectral characteristic component selector filter (40) selects for analysis at least one specific spectral component by referencing a base of stored knowledge of operational characteristics of the motor. A neural network (50) associates without supervision the selected detected spectral component with an operating condition of the motor (12). The base of stored knowledge is updated with new operating condition associations which have been made during real-time monitoring. A post processor (60) may enunciate the association to a user via an output device and it may generate a control signal to operate electrical distribution system protection or control apparatus.

Method and apparatus for predicting electric induction machine failure during operation

A system and method is provided for monitoring the operating condition of a pump by evaluating fault data encoded in the instantaneous current of the motor driving the pump. The data is converted to a frequency spectrum which is analyzed to create a fault signature having fault attributes relating to various fault conditions associated with the pump. The fault signature is then input to a neural network that operates in conjunction with a preprocessing and post processing module to perform decisions and output those decisions to a user interface. A stand alone module is also provided that includes an adaptive preprocessing module, a one-shot unsupervised neural network and a fuzzy based expert system to provide a decision making module that operates with limited human supervision.

Detection of pump cavitation/blockage and seal failure via current signature analysis

A monitoring diagnostic apparatus for detecting an abnormality occurring in an object being monitored such as electrical equipment and determining the cause of the abnormality is disclosed. First of all, vibration or partial discharge occurring in the monitored object is detected by using a sensor installed in close proximity to the monitored object. A detection signal output by the sensor then undergoes predetermined signal processing such as the Fourier transform and normalization. After the predetermined signal processing is completed, a neural network identifies the abnormality occurring in the monitored object and determining the cause of the abnormality. The neural network carries out a learning process based on causes of abnormalities occurring in the monitored object, outputting signals corresponding to the causes of the abnormalities.

Monitoring diagnostic apparatus using neural network

A method for determining whether a current state of a process variable output signal is within acceptable limits includes the following steps: establising reference data by sampling a plant's process variable output signal when the plant is operating at steady state and analyzing the sampled output signal to derive normalized reference data including an energy content of each of a plurality of frequency components of the sampled output signal. The procedure establishes a current operational data base by sampling a process variable signal when the plant is in operation. The sampled current output signal is analyzed to derive current data including a normalized energy content of each of a plurality of its frequency components. For each common frequency component of the reference data and the current data, the procedure compares their normalized energy contents and issues a non-steady state signal if the comparison indicates that the compared energy contents exceed a predetermined limit.

Plant parameter detection by monitoring of power spectral densities

A monitoring diagnostic device for an electrical appliance such as gas insulated switchgear includes a sensor, such as an acceleration sensor, and a neural network including an input layer, an intermediate layer, and an output layer, each consisting of a plurality of neural elements. The input, intermediate and output layers are coupled to each other via a plurality of connection weights. The output of the sensor is first processed and then is supplied to the neural elements of the input layer. The connection weights are adjusted by means of learning data such that the output from the neural elements of the output layer of the neural network correctly identifies the causes of abnormality of the electrical appliance.

Monitoring diagnosis device for electrical appliance

A neural network system includes input, intermediate and output layers, each layer containing at least one neural network element, each having an input and output, for simulating a neuron; and a plurality of inter-layer connections between neural elements wherein each input layer element has a connection to at least one intermediate layer element, and each intermediate layer element has a connection to at least one output layer element. Each inter-layer connection has a connecting weight. The system further includes sampling data and teaming data. The sampling data has pairs of values, each pair including an input value and corresponding output value, the input value having regular intervals. The learning data has at least three pairs of values, each pair including an input value and corresponding desired output value, the input values having irregular intervals. The intermediate layer elements are assigned unique sampling data value pairs and have unique sampling functions derived by translating original sampling functions by sampling data input values assigned to the neural elements. The sampling function defines a relationship between the input and output of the neural element. The connecting weight for each connection between an intermediate layer element and an output layer element is set to the sampling data output value assigned to the intermediate layer element. The system further includes a training mechanism that adjusts the connecting weights to minimize errors between learning data output values and actual output values obtained by applying the learning data input values to the neural network.

Akimoto Yoshiakira

Papers

Monitoring diagnosis device for electrical appliance

Neural network system with sampling data