Siemens Industry, Inc. (Alpharetta, GA)
Methods, mediums, and systems include the application of a system manager application in a data processor system to detect faults. A building automation system uses deep learning to receive point data. The data that is contaminated is used to train a deep-learning model to examine the hardware. Based on the model’s predictions and the points data that it has received the fault is detected within the hardware.
Building automation systems encompass various devices that assist in the control and monitoring of buildings’ operations. Security systems as well as fire safety systems comfort systems all form part of building automation systems. These systems control the environmental parameters such as heating and cooling, ventilation and air conditioning (“HVAC”), lighting, among others. A building automation system has several components that are scattered throughout the building. The HVAC system could include temperature sensors as well as control of ventilation damper. There could also be other components that are located in nearly every single part of the building. Building automation systems generally have one or more centralized control stations , from which system data may be monitored, and different aspects of the system’s operations can be controlled.
In order to allow monitoring and control of distributed control system components Building automation systems typically utilize multi-level communication networks to relay operational or alarm data between elements operating, such assensors and actuators, and the central control station. Several control stations connected via an Ethernet or other network may be distributed throughout one or more building locations, each having the ability to monitor and control the operation of the system.
A major purpose of a management system for building automation devices is the detection of faults, as well as other errors or unusual conditions within the system. Systems based on rules incorporate the fundamentals of building operations, the knowledge of experts in building and set out specific expectations for ranges of performance for building systems. However, this method has its drawbacks like being difficult to scale and lack of diagnostic data. FDD in the context of a BAS requires the rules to be predefined, and then manually configured with data from the BAS. The complexity of a rule will determine how complex it must be set up. The more complex the rule, there is the greater risk of making mistakes in its implementation. The development of better systems and procedures is desirable.
There are many disclosed embodiments that include media, methods and systems for fault detection of a building automation software employing deep learning. A building automation system may be given point data to analyze hardware. If the receivedpoint data is corrupted, it is possible to create a deep-learning model that can generate predicted data. The predicted data as well as receivedpoint data is then examined and analyzed to identify faults in the hardware.
A different embodiment is an untransitory computer-readable medium that is encoded with executable instructions that is programmed to run in the data processing system of an management system, which is configured to perform functions and execute the processes described in this. Another embodiment includes an automated building system that comprises an information processing system and a plurality of sensors, devices and actuators. The data processing system is executing a system manager application to performfunctions and to perform actions as described herein.
The data received must have at least the necessary points to allow an analysis on hardware. Training the deep-learning model could involve applying the Huber function. In various embodiments, training thedeep learning model includes applying dropout techniques to regularize to the received point data. In various embodiments, the process of analyzing the predicted data as well as the received point data includes include applying cumulative sum control chart (CUSUM)sequential analysis for summing, weighting, and detection of changes. In various embodiments, the manager application also normalizes certain or all of the data for example, normalizing the difference between the data predicted and the received point data or normalizing some or all of the predicted data or the received data. In various ways, the issue can be detected by comparing the received data with the predicted data for another time. In various ways, the fault can be detected when the normalized deviation between the predicted and received point data is greater than a predetermined threshold. In various embodiments the fault report can be a graphicuser interface illustrating the received data points in comparison to the expected data.
Here are some of the technical aspects and advantages that the disclosure offers. Those experienced in the field will be able to better understand the full description. Other features and benefits of thedisclosure will be described hereinafter that form the subject of the claims. Anyone of average skill in art will be able to make use of the disclosed ideas and embodiments as a basis to design or modify different structures to achieve the same purposes. These equivalent constructions are not out of line with the spirit and nature of the disclosure in its broadest sense.
It may be beneficial to define some terms or phrases prior to you start the DETAILED DESCRIPTION. The functionality associated with the controller may be distributed or centralized, either locally or remotely. This patent document gives definitions of specific words and phrases. Those of ordinary skill in the art of coding will recognize the meanings of these definitions to many, if not all situations to both past and future uses of such specific words or phrases. Although some terms can encompass a wide variety of examples, the claims could limit these terms to specific embodiments.
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