System and method for predicting shutdown alarms in boiler using machine learning

A system for anticipating shutdown alarms with respect to a boiler by way of machine learning model processing of information obtained by way of a plurality of internet of things (IoT) devices, the system comprising:

• a first controller supported on or proximate to the boiler;
• a plurality of sensors associated with the plurality of internet of things (IoT) devices and also supported on or positioned proximate to the boiler, wherein the sensors are configured to sense a plurality of parameters regarding the boiler and to provide a plurality of first signals regarding the sensed parameters to the controller;
• a gateway device of the boiler, wherein either the first signals or second signals based at least indirectly upon the first signals are sent to the gateway device;
• at least one communications device by which either the first signals, the second signals, or third signals based at least indirectly upon the first signals or second signals, are sent for receipt by a cloud computing system, for use in either developing one or more machine learning models for predicting the shutdown alarms or generating at least one prediction of one or more of the shutdown alarms by way of the one or more machine learning models;
• and
• a display configured to receive at least one fourth signal indicative of the at least one prediction of the one or more of the shutdown alarms, and to display either the at least one prediction or one or more alerts in response to the at least one prediction, by way of an application programming interface (API).

A method for anticipating shutdown alarms with respect to a boiler by way of a machine learning model, the method comprising:

• receiving and storing, at one or more storage devices, a plurality of types of boiler-related data,
• wherein the plurality of types of boiler-related data are received at least indirectly from a plurality of internet of things (IoT) devices associated with either the boiler or one or more additional boilers, and
• wherein the plurality of types of boiler-related data include each of firing rate data, oxygen level data, water level data, water temperature data, and fuel valve condition data;
• preprocessing and feature engineering the plurality of types of boiler-related data to arrive at a training data set;
• training the machine learning model at least in part based upon the training data set;
• deploying the trained machine learning model;
• receiving additional boiler-related data concerning the boiler and, by way of the deployed, trained machine learning model and based upon the additional boiler-related data, determining an alarm prediction concerning an anticipated alarm regarding the boiler;
• and
• taking at least one action based at least upon the prediction concerning the anticipated alarm regarding the boiler.