Abstract: A system and method for detecting faults and optimiz-ing power usage of solenoid valves. The method includes obtaining a current signature of the solenoid coil, using a dedicated circuit to detect various features and using a pulse width modulation controller optimize the power output of the system. Additionally, using machine learning, the system can be optimized using data from the dedicated circuit.

Abstract: The present disclosure relates to a system for monitoring a solenoid operated valve including at least one coil and at least one spool. The system compares a normalized current signature and a normalized current signature comparator to determine a spool fault condition.

Abstract: The present disclosure relates to a system for monitoring a solenoid operated valve including at least one coil and at least one spool.

Abstract: A method identifies electric load types of a plurality of different electric loads. The method includes providing a load feature database of a plurality of different electric load types, each of the different electric load types including a first load feature vector having at least four different load features; sensing a voltage signal and a current signal for each of the different electric loads; determining a second load feature vector comprising at least four different load features from the sensed voltage signal and the sensed current signal for a corresponding one of the different electric loads; and identifying by a processor one of the different electric load types by determining a minimum distance of the second load feature vector to the first load feature vector of the different electric load types of the load feature database.

Abstract: A method identifies electric load types of a plurality of different electric loads. The method includes providing a self-organizing map load feature database of a plurality of different electric load types and a plurality of neurons, each of the load types corresponding to a number of the neurons; employing a weight vector for each of the neurons; sensing a voltage signal and a current signal for each of the loads; determining a load feature vector including at least four different load features from the sensed voltage signal and the sensed current signal for a corresponding one of the loads; and identifying by a processor one of the load types by relating the load feature vector to the neurons of the database by identifying the weight vector of one of the neurons corresponding to the one of the load types that is a minimal distance to the load feature vector.

Abstract: A method identifies electric load types of a plurality of different electric loads. The method includes providing a hierarchical load feature database having a plurality of layers; including with each of a plurality of the layers a corresponding load feature set, the corresponding load feature set of at least one of the layers being different from the corresponding load feature set of at least another one of the layers; including with one of the layers a plurality of different electric load types; sensing a voltage signal and a current signal for each of the different electric loads; determining at least four different load features from the sensed voltage signal and the sensed current signal for a corresponding one of the different electric loads; and identifying by a processor one of the different electric load types by relating the different load features to the hierarchical load feature database.

Abstract: A method identifies electric load types of a plurality of different electric loads. The method includes providing a self-organizing map load feature database of a plurality of different electric load types and a plurality of neurons, each of the load types corresponding to a number of the neurons; employing a weight vector for each of the neurons; sensing a voltage signal and a current signal for each of the loads; determining a load feature vector including at least four different load features from the sensed voltage signal and the sensed current signal for a corresponding one of the loads; and identifying by a processor one of the load types by relating the load feature vector to the neurons of the database by identifying the weight vector of one of the neurons corresponding to the one of the load types that is a minimal distance to the load feature vector.

Abstract: A method identifies electric load types of a plurality of different electric loads. The method includes providing a load feature database of a plurality of different electric load types, each of the different electric load types including a first load feature vector having at least four different load features; sensing a voltage signal and a current signal for each of the different electric loads; determining a second load feature vector comprising at least four different load features from the sensed voltage signal and the sensed current signal for a corresponding one of the different electric loads; and identifying by a processor one of the different electric load types by determining a minimum distance of the second load feature vector to the first load feature vector of the different electric load types of the load feature database.