Autonomous machine learning diagonostic system with simplified sensors for home appliances

A computer implemented method for remote performance monitoring of a ventilation system comprising:

• accessing first sensor data from a first sensor placed on a return air grill for measuring a return temperature and a return humidity of air going into a return of the ventilation system for heating, cooling or a combination thereof;
• accessing second sensor data from a second sensor placed on a supply air grill for measuring a supply temperature and a supply humidity of air coming out of the ventilation system;
• using only data from the first sensor and data from the second sensor to determine an on-off duty cycle of an
• on-time period during which the ventilation system is operating to change a temperature, humidity, or combination thereof, between the air as measured by the first sensor on the return air grill and the air as measured by the second sensor on the supply air grill, and
• off-time period during which the ventilation system is not operating to change a temperature, humidity, or combination thereof, between the air as measured by the first sensor on the return air grill and the air as measured by the second sensor on the supply air grill;
• and
• using supervised machine learning with event models built from previously measured datasets of sensor data with two or more datasets of the first sensor data, the second sensor data and the on-off duty cycle to predict that a component of the ventilation system is faulty.

A system for remote performance monitoring of a ventilation system, the system comprising:

• a computer memory capable of storing machine instructions;
• and
• a hardware processor in communication with the computer memory, the hardware processor configured to access the computer memory to execute the machine instructions to perform
• accessing first sensor data from a first sensor placed on a return air grill for measuring a return temperature and a return humidity of air going into a return of the ventilation system for heating, cooling or a combination thereof;
• accessing second sensor data from a second sensor placed on a supply air grill for measuring a supply temperature and a supply humidity of air coming out of the ventilation system;
• using data from the first sensor and data from the second sensor to determine an on-off duty cycle of an
• on-time period during which the ventilation system is operating to change a temperature, humidity, or combination thereof, between the air as measured by the first sensor on the return air grill and the air as measured by the second sensor on the supply air grill, and
• off-time period during which the ventilation system is not operating to change a temperature, humidity, or combination thereof, between the air as measured by the first sensor on the return air grill and the air as measured by the second sensor on the supply air grill;
• and
• using machine learning with two or more datasets of the first sensor data, the second sensor data and the on-off duty cycle to indicating a component of the ventilation system is faulty.

A system for remote performance monitoring of a ventilation system, the system comprising:

• a first wireless sensor placed on a return air grill for measuring a return temperature and a return humidity of air going into a return of the ventilation system for heating, cooling or a combination thereof, to provide supply temperature and humidity data to an autonomous fault detection computer system;
• and
• a second wireless sensor placed on a supply air grill for measuring a supply temperature and a supply humidity of air coming out of the ventilation system to provide supply temperature and humidity data to the computer system,
• wherein the computer system determines an on-off duty cycle of an
• on-time period during which the ventilation system is operating to change a temperature, humidity, or combination thereof, between the air as measured by the first wireless sensor on the return air grill and the air as measured by the second wireless sensor on the supply air grill, and
• off-time period during which the ventilation system is not operating to change a temperature, humidity, or combination thereof, between the air as measured by the first wireless sensor on the return air grill and the air as measured by the second wireless sensor on the supply air grill;
• and
• using machine learning with two or more datasets of the data from the first wireless sensor, data from the second wireless sensor, and the on-off duty cycle to predict a component of the ventilation system is faulty.