On edge machine learning we are constantly dealing with non-stationary environments. These non-stationary environments can range from changes in temperature or humidity in sensors to changes in the way a machine, such as a gas turbine, is operating.
Time dependent data often experience variations on trends. Ekkono’s change detector can detect those variations so that the model can either adapt to the new trends or signal an alarm when a change occurs. Through our experience with different customers in many different industries, we have discovered that detecting a malfunction or a change in the behaviour of the device ahead of time is time and money-saving.
At Ekkono, we have built a change detector that can monitor data distributions in real-time, and signal when a change is happening. The uniqueness of the change detector is that it can be calibrated and run both incrementally in a microcontroller and batch-trained on a standard server. We provide two types of change detectors. The first type is one dimensional and runs directly over the sensor value. The second type is multidimensional, and runs on top of a machine learning model, being able to detect concept drift on multivariate data.