Abstract: A system for remote monitoring of a machine is provided. The system includes a data store to store machine data associated with an operation of the machine. The system includes an analyzer comprising a plurality of analytics engines to analyze the machine data. The analyzer selects one or more analytics engines based at least on one of machine data and a type of the machine. The analyzer is further configured to analyze machine data using the selected one or more analytics engines and to determine a plurality of exceptions. The system includes a rules engine to process at least two of the plurality of exceptions and determine a smart exception, wherein the smart exception is a hierarchical combination of the at least two of the plurality of exceptions. The system includes an interface to display a notification to a user in the event of a smart exception.

Abstract: A system for remote monitoring of a machine is provided. The system includes a data store to store machine data associated with an operation of the machine. The system includes an analyzer comprising a plurality of analytics engines to analyze the machine data. The analyzer selects one or more analytics engines based at least on one of machine data and a type of the machine. The analyzer is further configured to analyze machine data using the selected one or more analytics engines and to determine a plurality of exceptions. The system includes a rules engine to process at least two of the plurality of exceptions and determine a smart exception, wherein the smart exception is a hierarchical combination of the at least two of the plurality of exceptions. The system includes an interface to display a notification to a user in the event of a smart exception.

Abstract: A method of predicting life of a component of a machine is provided. The method includes receiving input data associated with the machine from a data repository. The input data include data associated with machine events and repairs. The method further includes analyzing the input data. Analyzing the input data includes determining a time difference between the machine event and the repair associated with the machine event, a repair reoccurrence, and the repair and a repair of an associated component. Analyzing the input data includes mapping the data associated with the machine event and repair based on the time difference, determining a weightage of a parameter for the machine events and the repairs based on the mapping, and computing a confidence rate of the machine event and the repair based on the weightage. The method further includes generating maintenance data of the component based on the confidence rate.

Abstract: The present invention provides a two stage process for treating wastewater that subjects the influent to anoxic and oxic filtering processes. The first stage performs anoxic nitrification i.e., the conversion of nitrate to nitrogen gas by bacteria operating in the absence of oxygen. Influent (effluent from the primary clarifiers) is fed into the bottom of the anoxic dentirification tank, preventing contact with the atmosphere and thus reducing the amount of unwanted dissolved oxygen. The influent passes upwards through a layer of sludge biomass before contacting bacteria coated media which performs the denitrification process. This arrangement permits the influent to contact the sludge and the biomass to circulate through the filter, enhancing deoxygenation and the performance of the system.