Determining states of an apparatus using support vector machines
The invention relates to a system and to a method for determining a state of a device by means of a trained support-vector machine. According to the invention, an operating parameter space is divided into classification volumes, at least one of which indicates a normal state and at least one other of which indicates a fault state of the device. A current state of the device can therefore be determined by determining where a current operating parameter point is to be arranged in the operating parameter space. The invention further relates to methods and to variants of the system in order to facilitate a cause evaluation and to determine particularly relevant operating parameters for the fault determination.
1. A system for determining a state of an apparatus, the system comprising:
a capture device configured to capture at least two operating parameters of the apparatus during operation of the apparatus; and
a computing device configured to implement an operating point module, a trained support vector machine (SVM), and an output module,
wherein the operating point module is configured to generate an operating point in an n-dimensional operating parameter space from the at least two captured operating parameters, where n is greater than or equal to two,
wherein the trained SVM is configured and trained to divide the n-dimensional operating parameter space into at least three classification volumes, each of the at least three classification volumes indicating different states of the apparatus,
wherein a first classification volume indicates a normal state of the apparatus, and a second classification volume and a third classification volume indicate different fault states of the apparatus,
wherein the trained SVM is further configured to assign the operating point generated by the operating point module to one classification volume of the at least three classification volumes,
wherein the output module is configured to:
determine a state of the apparatus according to the one classification volume to which the generated operating point is assigned by the trained SVM; and
output an output signal indicating at least the determined state of the apparatus, and
wherein the computing device is further configured to implement an evaluation module, the evaluation module being configured to:
determine a respective normal vector to every plane or hyperplane that separates the first classification volume, which identifies the normal state of the apparatus, from one of the classification volumes that indicate a fault state of the apparatus; and
determine and output, for each of the determined normal vectors, a value of an entry with a greatest absolute value for the normal vector.
2. The system of claim 1 , wherein the at least two operating parameters captured by the capture device comprise:
an electrical voltage;
an electrical current intensity;
an acceleration;
a linear acceleration;
a rotational speed;
a rotational acceleration;
a temperature; or
any combination thereof.
3. The system of claim 1 , wherein the SVM is configured to use a linear kernel.
4. The system of claim 1 , wherein the capture device is configured to capture the at least two captured operating parameters as parts of a respectively corresponding operating parameter maximum value.
5. An apparatus comprising:
a system for determining a state of the apparatus, the system comprising:
a capture device configured to capture at least two operating parameters of the apparatus during operation of the apparatus; and
a computing device configured to implement an operating point module, a trained support vector machine (SVM), and an output module,
wherein the operating point module is configured to generate an operating point in an n-dimensional operating parameter space from the at least two captured operating parameters, where n is greater than or equal to two,
wherein the trained SVM is configured and trained to divide the n-dimensional operating parameter space into at least three classification volumes, each of the at least three classification volumes indicating different states of the apparatus,
wherein a first classification volume indicates a normal state of the apparatus, and a second classification volume and a third classification volume indicate different fault states of the apparatus,
wherein the trained SVM is further configured to assign the operating point generated by the operating point module to one classification volume of the at least three classification volumes,
wherein the output module is configured to:
determine a state of the apparatus according to the one classification volume to which the generated operating point is assigned by the trained SVM; and
output an output signal indicating at least the determined state of the apparatus, and
wherein the computing device is further configured to implement an evaluation module, the evaluation module being configured to:
determine a respective normal vector to every plane or hyperplane that separates the first classification volume, which identifies the normal state of the apparatus, from one of the classification volumes that indicate a fault state of the apparatus; and
determine and output, for each of the determined normal vectors, a value of an entry with a greatest absolute value for the normal vector.
6. A method for determining a state of an apparatus the method comprising:
operating the apparatus;
capturing at least two operating parameters of the apparatus during operation of the apparatus;
generating an operating point in an n-dimensional operating parameter space based on the at least two captured operating parameters, where n is greater than or equal to two;
dividing the n-dimensional operating parameter space into at least three classification volumes using a trained support vector machine (SVM), each of the at least three classification volumes indicating different states of the apparatus, wherein a first classification volume of the at least three classification volumes indicates a normal state of the apparatus, and a second classification volume and a third classification volume of the at least three classification volumes indicate different fault states of the apparatus;
assigning the generated operating point to a classification volume of the at least three classification volumes;
determining a state of the apparatus according to the classification volume to which the generated operating point is assigned;
outputting an output signal indicating at least the determined state of the apparatus;
determining a respective normal vector to every plane or hyperplane that separates the first classification volume from one of the classification volumes that indicate a fault state of the apparatus; and
determining and outputting a value of an entry with a greatest absolute value for each determined normal vector.