Unsupervised Machine Monitoring System
Disclosed herein are methods and systems for the unsupervised, non-intrusive monitoring of motorized machines on a shop floor. A statistical algorithm is used to train itself to distinguish between an inactive and an active state of each monitored machine. The systems and methods use inexpensive sensors, do not intervene in the operation of the machines upon installation, would not adversely affect the operation of the machines, if the monitoring system would fail, do give reliable reports, and do not require any more than negligible human training.
1 . A method of monitoring activity and inactivity of a motor, the method comprising:
non-intrusively attaching an analog sensor to at least one cable of an electric motor;
applying unsupervised statistical analysis to an output signal of the sensor;
computing statistical parameters of the signal when the motor is not active; and
deriving, from the statistical computation, an indication of whether the motor is active.
2 . The method of claim 1 , wherein the statistical analysis is provided at least in part by an expectation-maximization algorithm.
3 . The method of claim 1 , wherein the analog sensor is a current sensor.
4 . The method of claim 1 , wherein the analog sensor is a voltage sensor.
5 . The method of claim 1 , wherein the analog sensor is a power sensor.
6 . A motor activity monitoring system comprising:
at least one analog sensor attachable to a phase cable of a motor; and
a processor configured to apply an unsupervised statistical analysis of an output signal of the sensor and to produce an indication of whether the motor is active or inactive.
7 . The motor of claim 6 , wherein the statistical analysis is provided at least in part by an expectation-maximization algorithm.
8 . The motor of claim 6 , wherein the analog sensor is a current sensor.
9 . The motor of claim 6 , wherein the analog sensor is a voltage sensor.
10 . The motor of claim 6 , wherein the analog sensor is a power sensor.