Systems and methods for detecting and removing material deposit

Abstract

To provide a system for monitoring at least one component.SOLUTION: A system includes at least one processor in communication with at least one memory device. The at least one processor is programmed to store a plurality of pieces of baseline information associated with the at least one component to be monitored and receive a plurality of current images of the at least one component to be monitored. The at least one processor is also programmed to detect a deviation from baseline based upon a comparison of the plurality of current images and the plurality of pieces of baseline information. The at least one processor is further programmed to classify the deviation and implement a corrective action based on the classification of deviation.SELECTED DRAWING: Figure 7

Description

[Technical field]

[0001] The present invention relates generally to material accretion detection and removal, and more particularly to a system for detecting the accumulation and accretion of undesirable materials on machines and equipment such as turbines.

[0002] Components and subassemblies of industrial machines and devices are routinely inspected for wear, potential defects, and the accumulation of foreign material. However, during operation of equipment such as turbines, it can be difficult to detect the accumulation of ice on the equipment in order to activate de-icing systems only when necessary to prevent icing damage to the equipment. Ice damage can be costly and time consuming to repair. Additionally, current conservative icing boundaries can cause unnecessary de-icing events and / or other modifications to the operation of the device, generally reducing the power output and / or performance of the user. Additionally, many current detection systems rely primarily on theoretical curves, which can change based on the equipment model and / or operating conditions. Thus, it would be desirable to have a system that facilitates improved detection of material accumulation and fouling in a manner that is not dependent on a particular equipment model and / or operating geometry. Summary of the Invention

[0003] In one aspect, a system for monitoring at least one component is provided. The system includes at least one processor in communication with at least one memory device. The at least one processor is programmed to store a plurality of baseline information associated with the at least one component being monitored and to receive a plurality of current images of the at least one component being monitored. The at least one processor is also programmed to detect a deviation from the baseline based on a comparison of the plurality of current images to the plurality of baseline information. The at least one processor is further programmed to classify the deviation and implement a corrective action based on the classification of the deviation. The system may include additional or alternative features, including those discussed elsewhere herein.

[0004] In another aspect, a method of monitoring at least one component in an apparatus is provided. The method is implemented on a computer system including at least one processor in communication with at least one memory device. The method includes storing a plurality of baseline information associated with the at least one component being monitored and receiving a plurality of current images of the at least one component being monitored. The method also includes detecting a deviation from the baseline based on the plurality of current images and the plurality of baseline information. The method further includes classifying the deviation and implementing a corrective action based on the classification of the deviation. The method may include additional or alternative features, including those discussed elsewhere herein.

[0005] The advantages will become more apparent to those skilled in the art from the following description of preferred embodiments shown and described by way of example. As will be understood, the present embodiments are capable of other and different embodiments, and their details are capable of modification in various respects. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive. [Brief description of the drawings]

[0006] The figures described below depict various aspects of the systems and methods disclosed therein. It is to be understood that each figure depicts an embodiment of a particular aspect of the disclosed systems and methods, and each figure is intended to correspond to a possible embodiment thereof. Additionally, wherever possible, the following description will refer to reference numerals contained in the following figures, where features depicted in multiple figures are designated with consistent reference numerals.

[0007] The drawings show the arrangement currently being discussed; however, it is understood that the present embodiment is not limited to the exact arrangement and the apparatus shown: [Figure 1] 1 is a schematic diagram of an exemplary high value asset, namely a turbomachine, and more specifically, in an exemplary embodiment, a turbine engine. [Diagram 2] 1 is a schematic diagram of an exemplary gas turbine engine. [Diagram 3] 3 illustrates an exemplary system for use in monitoring the operation of the turbine illustrated in FIGS. 1 and 2. [Figure 4] 4 illustrates an exemplary computer system that can be used with the system illustrated in FIG. 3. [Diagram 5] FIG. 2 illustrates an exemplary configuration of an exemplary user computing device, according to one embodiment of the present disclosure. [Figure 6] FIG. 2 illustrates an exemplary configuration of a server computer device according to one embodiment of the present disclosure. [Figure 7] 5 illustrates an exemplary process for deviation monitoring using the system illustrated in FIGS. 3 and 4. [Figure 8] 5 illustrates another exemplary process for monitoring deviations using the system illustrated in FIGS. 3 and 4. [Figure 9] FIG. 5 shows a number of images and corresponding deviations using the system shown in FIGS. 3 and 4.

[0008] Unless otherwise indicated, the drawings provided herein are intended to illustrate features of embodiments of the present disclosure. These features are believed to be applicable in a wide variety of systems that include one or more embodiments of the present disclosure. As such, the drawings are not intended to include all conventional features known by those skilled in the art to be necessary for the implementation of the embodiments disclosed herein. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] The present embodiments relate particularly to the detection and removal of material buildup, and more particularly to a system for detecting the buildup and buildup of undesirable materials on machines and equipment, such as turbines. In one exemplary embodiment, the process may be performed by a deviation monitoring system ("DMS") computing device. In an exemplary embodiment, the DMS computing device may be in communication with one or more client devices, one or more control systems, and / or sensors monitoring the machines and / or equipment.

[0010] In an exemplary embodiment, the system includes an optical camera and lighting system mounted in a gas turbine (GT) inlet plenum. The DMS computing device receives images from the optical camera. The system may also alternatively or additionally use other imaging devices, such as, but not limited to, a thermal camera and / or a spectrometer. The DMS computing device analyzes the images in real time and detects deviations compared to a baseline associated with the GT. For example, the DMS computing device may estimate ice accumulation on compressor blades (e.g., inlet guide vanes - IGVs) based on the comparison. The DMS computing device may distinguish between glaze and rime ice, and based on the amount of ice detected, the DMS computing device autonomously recommends one or more actions to control the anti-icing / de-icing of the system. In some embodiments, the DMS computing device may actively control and / or actuate the anti-icing system.

[0011] In at least one embodiment, the DMS computing device includes one or more machine learning (ML) trained models programmed to detect and identify material deposited and / or accumulated on the machine based on images provided by one or more cameras. In some embodiments, the DMS computing device also receives information from one or more additional sensors that facilitate identification of the buildup and provide context and additional information. Such additional sensors may include, but are not limited to, sensors measuring rotor speed, temperature, pressure, humidity, altitude, position, and / or any other operating variables that may assist in making the buildup identification as described herein. The DMS computing device combines different sensor modalities to autonomously detect any deviations from a baseline, then characterizes the type of deviations in real time, performs a quantification of the deviation, and communicates with one or more control systems.

[0012] The DMS computing device "trains" one or more models to detect, classify, and quantify deviations from a baseline of the machine and / or equipment. The DMS computing device monitors machines, such as the GT inlet and compressor, and detects deviations from the baseline. The DMS computing device characterizes the type of deviation (e.g., ice occurrence) in real time. "Real time" refers to either the time of occurrence of a relevant event, the time of measurement and collection of certain data, the time of processing of data, or the time of the system's response to events and environments. In the embodiments described herein, these activities and events occur substantially instantaneously. The DMS computing device performs quantification of deviations (e.g., affected areas, blades, etc.). The DMS computing device determines if and when preventive and / or corrective measures should be taken. More specifically, in an exemplary embodiment, the DMS computing device determines when the machine's control system should be activated to implement one or more preventive and / or corrective measures (at least one corrective and / or preventative measure). Such corrective actions may involve activation of a de-icing system using high temperature inlet bleed air. Further corrective actions may include, but are not limited to, increasing rotational speed, ordering or scheduling maintenance to repair detected leaks, ordering or scheduling in-line cleaning, etc.

[0013] In some embodiments, the DMS computer system operates in a closed loop, where the DMS computer system controls and / or initiates preventive and / or corrective actions, in other embodiments, the DMS computer system operates in an open loop, where the DMS computer system notifies operators of identified equipment and / or machine conditions at risk for buildup and the need for corrective and / or preventive actions.

[0014] In some embodiments, the DMS computer system is an edge-based computer system. An edge-based computer system allows devices in remote locations to process data at the "edge" of the network by the device or a local server. Furthermore, when data needs to be processed at a central data center, only the most critical data is sent, thereby minimizing latency. In other embodiments, the DMS computer system is configured as a cloud-based computing architecture that allows for both local and fleet management.

[0015] While the above describes the use of the systems and processes described herein to monitor the deposition of materials on machines and equipment, one of ordinary skill in the art will appreciate that these systems and methods can also be used to classify and quantify deviations from a baseline of a machine and / or equipment, such as, but not limited to, damage to one or more parts (components) of the machine, the need for calibration of one or more parts of the machine, missing parts of the machine, leaks, and / or other variations and / or deviations from the baseline of the machine and / or equipment.

[0016] At least one of the technical problems addressed by the system includes: (i) early detection and / or estimation of ice and other material formation; (ii) enabling de-icing systems or other measures to be activated only when necessary to avoid damage to machinery, thereby facilitating a reduction in unnecessary activation of such systems or measures; (iii) improving user output and performance at various loads by eliminating conservative ice boundaries; (v) providing monitoring coverage to individual equipment and / or fleets; (vi) improving the accuracy of material accumulation on machinery while in operation; and / or (vii) significantly reducing the need for human intervention and monitoring while equipment is in operation.

[0017] The technical effects of the systems and processes described herein may be achieved by performing at least one of the following steps: a) storing a plurality of baseline information for one or more monitored components, b) receiving a plurality of current images of the one or more monitored components, c) detecting a deviation from the baseline based on the plurality of images and the plurality of baseline information, d) executing at least one classification model to generate a classification of the deviation, e) determining an amount of the deviation based on the classification of the deviation, and f) activating at least one mitigation system based on the determined amount of the deviation.

[0018] The monitoring methods and systems described herein provide accurate predictions of component life. Additionally, the methods and systems described herein facilitate accurate prediction of potential failure points of those components. Additionally, the systems and methods described herein are not limited to a single component, but can be implemented with any component of an apparatus that is subject to wear and / or the accumulation of undesirable materials. For example, the methods and systems described herein can be used with any component of an aircraft, wind turbine, locomotive, power system, automobile, and / or other apparatus, including components that are subject to wear and / or the accumulation of undesirable materials. By aggregating data regarding the life cycle of multiple parts (components) of the same type, operational life and maintenance schedules are generated more accurately.

[0019] 1 is a schematic diagram of an exemplary high-value asset, i.e., a turbomachine, and more specifically, in an exemplary embodiment, a turbine engine 100. Alternatively, any other devices, systems, and equipment that may be monitored and subject to maintenance due to components that may be subject to wear and / or accumulation of undesirable materials may be subject to the methods and systems described herein, including, but not limited to, combustion systems, such as furnaces, gasification systems, boilers, turbines, geared devices, and / or high-temperature exhaust systems.

[0020] In the exemplary embodiment, gas turbine engine 100 includes an intake section 102 and a compressor section 104 coupled downstream from and in flow communication with intake section 102. Compressor section 104 is enclosed within a compressor casing 106. A combustor section 108 is downstream from and in flow communication with combustor section 104, and a turbine section 110 is downstream from and in flow communication with combustor section 108. Gas turbine engine 100 is enclosed within a turbine casing 112 and includes an exhaust section 114 downstream from turbine section 110. Further, in the exemplary embodiment, turbine section 110 is coupled to compressor section 104 via a rotor assembly 116 that includes, but is not limited to, a compressor rotor or drive shaft 118 and a turbine rotor or drive shaft 120.

[0021] In the exemplary embodiment, combustor section 108 includes a plurality of combustor assemblies, i.e., combustors 122, each coupled in flow path communication with compressor section 104. Combustor section 108 also includes at least one fuel nozzle assembly 124. Each combustor 122 is in flow path communication with at least one fuel nozzle assembly 124. Additionally, in the exemplary embodiment, turbine section 110 and compressor section 104 are rotatably coupled to a load 126 via drive shaft 118. Load 126 may include, for example, but is not limited to, an electrical generator or a mechanical drive application, such as a pump. Alternatively, gas turbine engine 100 may be an aircraft engine. In the exemplary embodiment, compressor section 104 includes at least one rotatable compressor blade assembly 128, i.e., one or more blades 128 and at least one adjacent stationary vane assembly 130.

[0022] Also, in the exemplary embodiment, turbine section 110 includes at least one rotatable turbine blade assembly, or turbine bucket, 132 and at least one adjacent static nozzle assembly 134. Each compressor blade assembly 128 and each turbine bucket 132 is coupled to rotor assembly 116, and more specifically, to compressor drive shaft 118 and turbine drive shaft 120.

[0023] During operation, the intake section 102 channels intake air 136 towards the compressor section 104. The compressor section 104 compresses the intake air 136 to a higher pressure and temperature before discharging the compressed air 138 towards the combustor section 108. The compressed air 140 is channeled to the fuel nozzle assembly 124, mixed with fuel (not shown), combusted in each combustor 122 to generate combustion gases 142, and channeled downstream towards the turbine section 110. The combustion gases 142 generated in the combustors 122 are channeled downstream towards the turbine section 110. After impacting the turbine buckets 132, the thermal energy is converted to mechanical rotational energy that is used to drive the rotor assembly 116. The turbine section 110 drives the compressor section 104 and the load 126 via the drive shafts 118 and 120, and the exhaust gases 144 are exhausted to the surrounding atmosphere via the exhaust section 114.

[0024] 2 is a schematic diagram of an exemplary gas turbine engine, and more specifically, in an exemplary embodiment, a jet turbine engine 150. In the exemplary embodiment, jet turbine engine 150 includes a fan assembly 152, a booster 154, a high-pressure compressor 156, and a combustor 158. Jet turbine engine 150 also includes a high-pressure turbine 160, and a low-pressure turbine 162, all coupled in a serial flow arrangement. Fan assembly 152 includes an array of rotatable fan blades 164 extending radially outward from a rotor disk 166. Turbine engine 150 also includes an intake side 168 and an exhaust side 170. In the exemplary embodiment, turbine engine 150 may be, but is not limited to, a LEAP or P20 gas turbine engine commercially available from General Electric Company, Cincinnati, Ohio. Fan assembly 152, booster 154, and low-pressure turbine 162 may be coupled via a first rotor shaft 172, and high-pressure compressor 156 and high-pressure turbine 160 may be coupled via a second rotor shaft 174.

[0025] During operation, air flows through fan assembly 152 and compressed air is provided through booster 154 to high pressure compressor 156. The high pressure compressed air is delivered to combustor 158 where the air is mixed with fuel and ignited to generate combustion gases. The combustion gases are channeled from combustor 158 to drive high pressure turbine 160 and low pressure turbine 162. Low pressure turbine 162 drives fan assembly 152 and booster 154 by way of a first rotor shaft 172. High pressure turbine 160 drives high pressure compressor 156 via a second rotor shaft 174. In at least the exemplary embodiment, the multiple components include blades 164.

[0026] 1 and 2 are described to illustrate an exemplary device that may be subject to the accumulation and accretion of undesirable materials, such as ice, snow, dust, dirt, and / or oil. Both the turbine engine 100 and the jet turbine engine 150 include multiple components that may be subject to wear and may be adversely affected by the accumulation and accretion of materials. For example, the compressor blade assembly 128, including one or more blades 128 and stationary vane assembly 130 of the turbine engine 100, and the blades 164 of the jet turbine engine 150 are non-limiting examples of exemplary components that may be subject to wear and / or may be affected by the accumulation and accretion of undesirable materials. In an exemplary embodiment, the decision to implement at least one corrective action and / or preventive action is determined taking into consideration the current operating conditions of the monitored components. As described further in this disclosure, determining any changes to the baseline of the machine and / or equipment components in real time allows for timely action to correct potential problems. For example, if a comparison of baseline and current images of a machine component reveals an accumulation of what is identified and quantified as an amount of ice on the component that exceeds a predefined threshold according to a predefined protocol, the system may initiate at least one de-icing countermeasure. As used herein, an accumulation of ice in an amount that exceeds a predefined threshold may be referred to as "potentially harmful."

[0027] 3 illustrates an exemplary system 300 that may be used to monitor the operation of turbines 100 and 150 (shown in FIGS. 1 and 2). System 300 illustrates one monitoring system according to at least one embodiment. In the exemplary embodiment, within system 300, compressor section 104 is monitored.

[0028] As shown in FIG. 3, in an exemplary embodiment, turbines 100 and 150 include an inlet system 302 that extends inlet section 102 (shown in FIG. 1). Inlet system 302 includes an air duct 304 that includes an inlet silencer 306, which in an exemplary embodiment may be one or more sections of parallel baffles, followed by a heat manifold or piccolo pipe 308. In at least one embodiment, inlet silencer 306 is a baffle-type structure that facilitates attenuation of high frequency noise in the air inlet caused by the rotating compressor blades. Air duct 304 leads to a gas turbine inlet plenum 310 that includes compressor casing 106.

[0029] A window 312 or other opening is defined in a wall of the gas turbine inlet plenum 310. The window 312 allows an illumination system 314 to illuminate the compressor section 104, including one or more blades 128 and stationary vane assembly 130 of the compressor section 104. A camera 316 and / or any other optical sensor that enables the systems 300 and 400 to function can view the compressor section 104 through the window 312 and collect images of the compressor section 104 during operation. In some embodiments, the camera 316 may be at least one of, but is not limited to, an optical camera, a thermal camera, and a spectroscope. The images and other sensor information are transmitted to a deviation monitoring system (DMS) computing device 320. The images and other sensor information may include infrared and / or spectral imaging information.

[0030] In at least some embodiments, the coupled device sensors 318 for the turbines 100 and 150 can collect additional turbine sensor data 322 for the turbines 100 and 150, such as, but not limited to, discharge pressure, compressor discharge pressure, discharge temperature, IGV position, rotor speed, temperature, pressure, and / or the humidity. The DMS computing device 320 receives the turbine sensor data 322, and in some embodiments, the DMS computing device 320 uses the turbine sensor data 322 to determine context for the components being monitored based on images transmitted by the camera 316. For example, ice may only form in certain temperature and / or humidity ranges. If the current operating environment temperature and / or humidity measured via the turbine sensor data 322 is not within a predefined range where ice accumulation may occur, the DMS computing device 320 uses that information to determine what material is accumulating in the compressor section 104.

[0031] In the exemplary embodiment, DMS computer system 320 is in communication with turbine control system 324. Turbine control system 324 controls the operation of turbine 100 or 150. As such, turbine control system 324 also receives turbine sensor data 322 transmitted from device sensors 318.

[0032] In the exemplary embodiment, turbine control system 324 also controls one or more mitigation systems 326. Mitigation systems 326 are configured to modify and / or prevent conditions of monitored components within turbines 100 and / or 150 that may be adversely affected by the buildup or accumulation of materials such as ice, snow, dust, dirt, and / or oil, but are not limited to such. In some embodiments, mitigation systems 326 increase the operating speed of turbines 100 and / or 150 to help disperse the buildup.

[0033] In an exemplary embodiment, the turbine control system 324 also communicates with one or more client devices 328. The client devices 328 are associated with individual users and / or groups of users. In at least one embodiment, the turbine control system 324 transmits information regarding the operation of the turbines 100 and 150 to the users via the client devices 328. In some of these embodiments, the information is presented via a user interface, such as via a dashboard. In at least one embodiment, some of the information includes turbine sensor data 322.

[0034] In an exemplary embodiment, the DMS computing device 320 receives images from the camera 316 and, in response, analyzes the images to determine whether there is a buildup or accumulation of material in the compressor section 104, or other components being monitored, based on a comparison to a baseline. The DMS computing device 320 then classifies and determines what material is detected. In some embodiments, the DMS computing device 320 classifies and determines the material based on the turbine sensor data 322. The DMS computing device 320 also quantifies the detected material and determines whether the amount of accumulated material exceeds at least one predetermined threshold. If the threshold is met or exceeded, the DMS computing device 320 instructs the turbine control system 324 to activate a mitigation system 326.

[0035] In an exemplary embodiment, the DMS computing device 320 continues to monitor the compressor section 104 after the mitigation system is activated and determines when mitigation is complete and when to have the turbine control system 324 deactivate the mitigation system 326. In such an embodiment, the DMS computing device 320 determines that the mitigation system 326 should be deactivated when the remaining amount of material, as determined by a subsequent comparison between the additional images collected and a predefined threshold, is below a deactivation threshold or a low-risk threshold. Alternatively and / or additionally, the DMS computing device 320 can notify the user via the client device 328 of the risk of potentially damaging buildup and the need to activate the mitigation system 326. In response, depending on a predetermined operating protocol, the user can either instruct the turbine control system 324 to activate the mitigation system 326 or activate the mitigation system themselves.

[0036] 4 illustrates an exemplary computer system 400 that may be used with system 300 (shown in FIG. 3). In an exemplary embodiment, system 400 monitors the deposition and / or accumulation of material on one or more components of a machine and / or equipment. In some embodiments, system 400 analyzes images of the components to determine one or more conditions of the components. Additionally, system 400 is a deviation monitoring system (DMS) computer device 320 that detects, classifies, and quantifies deviations from baseline.

[0037] As described in more detail herein, the DMS computing device 320 (also known as the DMS server 320) is programmed to analyze the images to detect, classify, and quantify deviations from the baseline. Additionally, the DMS server 320 is programmed to continually update and "train" one or more models used to detect, classify, and quantify deviations from the baseline from the images and other sensor information. The DMS server 320 is programmed to a) store a plurality of baseline information for one or more monitored components, b) receive a plurality of current images of each monitored component, c) detect deviations from the baseline based on a comparison of the plurality of images to the plurality of baseline information, d) execute at least one classification model to generate a classification of the deviation, e) determine an amount of deviation based on the classification of the deviation, and f) activate at least one mitigation system based on the determined amount of deviation.

[0038] In an exemplary embodiment, the device sensors 318 may be any sensors capable of measuring a desired operating condition of the device. Examples of device sensors 318 include, but are not limited to, resonant sensors, such as gyroscopes, accelerometers, and pressure sensors, environmental sensors that measure environmental attributes, and system sensors. Examples of environmental attributes include, for a jet turbine, external temperature, humidity, and / or acceleration. Examples of system attributes of the turbines 100 and / or 150 include revolutions per minute (RPM), vibration, and internal temperature. In an exemplary embodiment, the device sensors 318 are in communication with the DMS server 320 and / or the device control system 410. The equipment sensors 318 connect to the DMS server 320 and / or the device control system 410 through a number of interfaces, including, but not limited to, a network such as a local area network (LAN) or wide area network (WAN), a dial-in connection, a cable modem, an Internet connection, wireless, and special high speed integrated services digital network (ISDN) lines. The device sensors 318 receive data regarding the status of devices such as the turbines 100 and 1500 and report those statuses to the DMS server 320 and / or the device control system 410.

[0039] In an exemplary embodiment, the DMS computing device 320 (also known as the DMS server 320) is a computer that includes a web browser or software application that enables the DMS server 320 to communicate with the client devices 328, device control systems 410, light sensors 405, and / or device sensors 318 using the Internet, a local area network (LAN), and / or a wide area network (WAN). In some embodiments, the DMS server 320 is communicatively coupled to the Internet via a number of interfaces, including, but not limited to, at least one of the Internet, a LAN, a WAN, or a network such as an integrated services digital network (ISDN), a dial-up connection, a digital subscriber line (DSL), a cellular connection, a satellite connection, and a cable modem. The DMS server 320 may be a desktop computer, a laptop computer, a PDA (Personal Digital Assistant), a mobile phone, a smartphone, a tablet, a phablet, a wearable electronic device, a smartwatch, a virtual headset or glasses (for example, but not limited to, an AR (Augmented Reality), VR (Virtual Reality), or XR (Augmented Reality) headset or glasses, which may be any device that can access a network such as the Internet), a chatbot, a voice bot, a ChatGPT bot or a ChatGPT-based bot, or other web-based connectable or mobile device.

[0040] In an exemplary embodiment, the mitigation system 326 is one or more systems programmed to modify and / or prevent the accumulation and / or deposition of undesirable substances on device components. In an exemplary embodiment, the mitigation system 326 is in communication with the DMS server 320 and / or the device control system 410 and is coupled to the DMS server 320 and / or the device control system 410 via a number of interfaces including, but not limited to, a network such as a local area network (LAN) or wide area network (WAN), a dial-in connection, a cable modem, an Internet connection, wireless, and / or a special high speed integrated services digital network (ISDN) line.

[0041] In an exemplary embodiment, the client device 328 is a computer including a web browser or software application that enables the client device 328 to communicate with the DMS server 320 and / or the device control system 410 using the Internet, a local area network (LAN), or a wide area network (WAN). In some embodiments, the client device 328 is communicatively coupled to the Internet through a number of interfaces including, but not limited to, the Internet, a LAN, a WAN, or a network such as an integrated services digital network (ISDN), a dial-up connection, a digital subscriber line (DSL), a cellular connection, a satellite connection, and / or a cable modem. The client device 328 may be a desktop computer, a laptop computer, a PDA (Personal Digital Assistant), a mobile phone, a smartphone, a tablet, a phablet, a wearable electronic device, a smart watch, a virtual headset or glasses (for example, but not limited to, any device that can access a network such as the Internet, an AR (Augmented Reality), VR (Virtual Reality), or XR (Extended Reality) headset or glasses), a chatbot, a voice bot, a ChatGPT bot or a ChatGPT-based bot, or any other web-based connectable device or mobile device.

[0042] In an exemplary embodiment, the optical sensor 405 communicates with the DMS server 320 and provides the DMS server 320 with an image of one or more components of the device. The optical sensor 405 includes, but is not limited to, a camera, an IR camera, a spectroscope, and / or any other optical sensor 405 that enables the systems 300 and 400 to function as described herein. In an exemplary embodiment, the optical sensor 405 is similar to the camera 316 (shown in FIG. 3). In an exemplary embodiment, the optical sensor 405 can communicate with the DMS server 320 using the Internet, a local area network (LAN), or a wide area network (WAN). In some embodiments, the optical sensor 405 is communicatively coupled to the Internet via a number of interfaces, including, but not limited to, at least one of the Internet, a LAN, a WAN, or a network such as an integrated services digital network (ISDN), a dial-up connection, a digital subscriber line (DSL), a cellular connection, a satellite connection, and a cable modem.

[0043] The device control system 410 may be any system that controls equipment such as the turbines 100 and 150 (shown in FIGS. 1 and 2). The device control system 410 communicates with the DMS server 320 and receives the turbine sensor data 322. The device control system 410 may be similar to the turbine control system 324. In an exemplary embodiment, the device control system 410 is a computer that includes a web browser or software application that allows the device control system 410 to communicate with the client devices 328 and the DMS server 320 using the Internet, a local area network (LAN), or a wide area network (WAN). In some embodiments, the device control system 410 is communicatively coupled to the Internet via a number of interfaces, including, but not limited to, the Internet, a LAN, a WAN, or a network such as an integrated services digital network (ISDN), a dial-up connection, a digital subscriber line (DSL), a cellular phone connection, a satellite connection, and / or a cable modem. The device control system 410 may be a desktop computer, a laptop computer, a personal digital assistant (PDA), a mobile phone, a smartphone, a tablet, a phablet, a wearable electronic device, a smart watch, a virtual headset or glasses (e.g., an AR (augmented reality), VR (virtual reality), or XR (extended reality) headset or glasses), a chatbot, a voice bot, a ChatGPT bot or a ChatGPT-based bot, or any other web-based connectable or mobile device.

[0044] The database server 415 is communicatively coupled to a database 420 that stores data. In one embodiment, the database 420 is a database that includes one or more detection, classification, and / or quantification models, thresholds, and / or baseline information. In some embodiments, the database 420 is stored remotely from the DMS server 320. In some embodiments, the database 420 is distributed. In an exemplary embodiment, a person can access the database 420 via a client device 328 by logging on to the DMS server 320.

[0045] 5 illustrates an exemplary configuration 500 of a user computing device 502 according to one embodiment of the present disclosure. In an exemplary embodiment, the user computing device 502 may be similar to or the same as the client device 328 and the DMS computing device 320 (both shown in FIG. 3). The user computing device 502 may be operated by a user 501.

[0046] The user computing device 502 may include a processor 505 for executing instructions. In some embodiments, the executable instructions may be stored in a memory area 510. The processor 505 may include one or more processing units (e.g., a multi-core configuration). The memory area 510 may be any device that allows for the storage and retrieval of information, such as executable instructions and / or transaction data. The memory area 510 may include one or more computer-readable media.

[0047] The user computing device 502 may also include at least one media output component 515 for presenting information to the user 501. The media output component 515 may be any component capable of conveying information to the user 501. In some embodiments, the media output component 515 may include an output adapter (not shown), such as a video adapter and / or an audio adapter. The output adapter is operably coupled to the processor 505 and may be operably coupled to an output device, such as a display device (e.g., a cathode ray tube (CRT), a liquid crystal display (LCD), a light emitting diode (LED) display, or an "electronic ink" display) or an audio output device (e.g., speakers or headphones).

[0048] In some embodiments, the media output component 515 may be configured to present a graphical user interface (e.g., a web browser and / or a client application) to the user 501. The graphical user interface may include, for example, an interface for viewing information provided by the device control system 410 (shown in FIG. 4). In some embodiments, the user computing device 502 may include an input device 520 for receiving input from the user 501. The user 501 may use the input device 520 to provide information either via speech or typing, without limitation.

[0049] The input device(s) 520 may include, for example, a keyboard, a pointing device, a mouse, a stylus, a touch-sensitive panel (e.g., a touchpad or touch screen), a gyroscope, an accelerometer, a position detector, a biometric input device, and / or a voice input device. A single component, such as a touch screen, may function as both an output device for the media output component 515 and as an input device 520.

[0050] The user computing device 502 may also include a communications interface 525 that is communicatively coupled to a remote device, such as the DMS server 320. The communications interface 525 may include, for example, a wired or wireless network adapter and / or a wireless data transceiver for use with a mobile communications network.

[0051] Stored in memory area 510 are computer readable instructions for, for example, providing a user interface to user 501 via media output component 515, and optionally receiving and processing input from input device 520. The user interface may include a web browser and / or a client application, among other possibilities. A web browser allows a user, such as user 501, to view and interact with media and other information typically embedded in web pages or websites from DMS server 320. A client application may allow user 501 to interact with, for example, DMS server 320. For example, instructions may be stored by a cloud service and output of execution of the instructions transmitted to media output component 515.

[0052] 6 illustrates an example configuration 600 of a server computing device 602 according to one embodiment of the disclosure. In an example embodiment, the server computing device 602 may be similar to or the same as the DMS server 320, the turbine control system 324 (all shown in FIG. 3), the device control system 410, and the database server 415 (all shown in FIG. 4). The server computing device 602 may also include a processor 605 for executing instructions. The instructions may be stored in a memory area 610. The processor 605 may include one or more processing units (e.g., a multi-core configuration).

[0053] The processor 605 may be operatively coupled to a communications interface 615 such that the server computing device 602 can communicate (e.g., using wireless communication or data transmission over one or more wireless links or digital communications channels) with remote devices, such as another server computing device 602, a DMS server 320, a mitigation system 326, and a client device 328 (all shown in FIG. 3). For example, the communications interface 615 may receive images from an optical sensor 405 (shown in FIG. 4) over the Internet, as shown in FIG.

[0054] The processor 605 may also be operatively coupled to a storage device 625. The storage device 625 may be, but is not limited to, any computer-operated hardware suitable for storing and / or retrieving data, such as data related to one or more models. In some embodiments, the storage device 625 may be integrated into the server computing device 602. For example, the server computing device 602 may include one or more hard disk drives as the storage device 625.

[0055] In other embodiments, storage device 625 may be external to server computing device 602 and may be accessed by multiple server computing devices 602. For example, storage device 625 may include multiple storage units, such as hard disks and / or solid state disks in a storage area network (SAN), a network attached storage (NAS) system, and / or a redundant array of inexpensive disks (RAID) configuration.

[0056] In some embodiments, processor 605 may be operatively coupled to storage device 625 via storage interface 620. Storage interface 620 may be any component capable of providing processor 605 with access to storage device 625. Storage interface 620 may include, for example, an Advanced Technology Attachment (ATA) adapter, a Serial ATA (SATA) adapter, a Small Computer System Interface (SCSI) adapter, a RAID controller, a SAN adapter, a network adapter, and / or any component that provides processor 605 with access to storage device 625.

[0057] Processor 605 can execute computer-executable instructions to implement aspects of the present disclosure. In some embodiments, processor 605 may be transformed into a special purpose microprocessor by executing computer-executable instructions or being otherwise programmed. For example, processor 605 may be programmed with instructions such as those illustrated in Figures 7 and 8.

[0058] 7 shows an example process 700 for monitoring deviations using systems 300 and 400 (shown in FIGS. 3 and 4). In an example embodiment, process 700 is performed by DMS server 320 (shown in FIG. 3).

[0059] In an exemplary embodiment, the DMS server 320 receives images from the optical sensor 405. The DMS server 320 also receives sensor data transmitted from the device sensor 318. In an exemplary embodiment, the DMS server 320 pre-processes 705 the images and sensor data to prepare information for processing 700. In at least some embodiments, the DMS server 320 also analyzes the sensor data transmitted from the device sensor 318 to determine 710 whether conditions are right for a deviation to occur. For example, if the temperature and / or humidity are outside of a predefined range where ice buildup may form, the DMS server 320 determines 710 that conditions are not right for a deviation to occur and determines 720 that no action is required other than continued monitoring.

[0060] If conditions exist such that a deviation may occur, i.e., the temperature and / or humidity are within ranges where such accumulation may occur, the DMS server 320 analyzes the images and sensor data to determine if any deviations are detected 715. In at least one embodiment, the DMS server 320 determines if there is a deviation or change from a baseline in the images for the analyzed component 715. In at least one embodiment, the DMS server 320 performs a pixel-by-pixel comparison of the images to determine if a deviation is detected 715. If no deviation is detected, the DMS server 320 determines 720 that no action is required and continues monitoring the process 700.

[0061] If a deviation 715 is detected, the DMS server 320 classifies the deviation 725. In at least one embodiment, the DMS server 320 runs one or more models to analyze the detected deviation and classify the deviation 725. The one or more models are continually updated and trained to reliably detect different types of deviations, including deviations of different materials. Exemplary deviations include, but are not limited to, adhesion and / or accumulation of foreign matter such as ice, snow, dirt, dust, and / or oil, damage to one or more parts (components) of the machine, the need to calibrate one or more parts of the machine, missing components or parts of components of the machine, the presence of leaks, and / or other variations and / or deviations from the baseline of the machine and / or equipment.

[0062] The DMS server 320 measures 730 the amount of deviations present. In at least one embodiment, the DMS server 320 measures 730 the amount of deviations present using the pixel count and classification of the detected deviations. The DMS server 320 then compares 735 and 740 the amount of deviations present to one or more thresholds. In some embodiments, the thresholds are predefined by one or more users. In yet another embodiment, the thresholds are different for different types of deviations. The DMS server 320 determines 735 whether the deviations exceed a low-risk threshold. If the deviations do not exceed the low-risk threshold, the DMS server 320 determines 720 that no action is required and continues monitoring the process 700. If the deviations exceed the low-risk threshold, the DMS server 320 determines 740 whether the deviations exceed a high-risk threshold. If the deviations do not exceed the high-risk threshold, the DMS server 320 continues measuring 730 the deviations.

[0063] If the deviation exceeds the high-risk threshold, the DMS server 320 activates 745 the mitigation system 326 (shown in FIG. 3). In at least one embodiment, after activating the mitigation system 326, the DMS server 320 continues to measure the deviation 750. The DMS server 320 determines 755 whether the deviation still exceeds the low-risk threshold. If the deviation still exceeds the low-risk threshold, the DMS server 320 continues to use the mitigation system 326. If the deviation no longer exceeds the low-risk threshold, the DMS server 320 deactivates the mitigation system 326.

[0064] In an exemplary embodiment, the process 700 is continuously repeated by the DMS server 320 based on images received from the optical sensor 405. In some embodiments, the DMS server 320 analyzes multiple images substantially simultaneously in the determination process 700. In these embodiments, the DMS server 320 analyzes images from a sliding window of time. For example, the sliding window of time can be 30 seconds, 500 seconds, and / or any other window of time that enables the system to operate as described herein.

[0065] In some embodiments, multiple corrective actions are available and the DMS server 320 decides which corrective action to take. In some embodiments, the DMS server 320 determines that a corrective action is insufficient, stops the corrective action, and initiates a new corrective action.

[0066] FIG 8 illustrates another exemplary process 800 for monitoring deviations using systems 300 and 400 (shown in FIGS. 3 and 4). In an exemplary embodiment, process 800 is performed by DMS server 320 (shown in FIG. 3). In an exemplary embodiment, process 700 is a closed-loop operation, while process 800 is an open-loop that allows user review of decisions recommended by DMS server 320. In at least one embodiment, DMS server 320 communicates with one or more client devices 328 (shown in FIG. 3) that can provide information to and receive input from users.

[0067] In an exemplary embodiment, the DMS server 320 receives images from the optical sensor 405. The DMS server 320 also receives sensor data from the device sensor 318. In an exemplary embodiment, the DMS server 320 pre-processes 805 the images and sensor data to prepare information for the process 700. In at least some embodiments, the DMS server 320 also analyzes 810 the sensor data from the device sensor 318 to determine whether conditions are appropriate for deviations to occur. For example, if the temperature and / or humidity are outside of a predefined range where ice buildup may form, the DMS server 320 determines 810 that the current conditions are not within the operating temperature and / or humidity range where deviations may occur. The DMS server 320 then determines 820 that no action is required and continues monitoring the process 800.

[0068] If conditions are right for a deviation to occur, the DMS server 320 analyzes the images and the sensor data to determine if a deviation is detected 815. In at least one embodiment, the DMS server 320 determines if there is a deviation or change from the baseline in the images for the analyzed component 815. In at least one embodiment, the DMS server 320 performs a pixel-by-pixel review of the images to determine if a deviation is detected 815. If no deviation is detected, the DMS server 320 determines 820 that no action is required and continues monitoring the process 800.

[0069] If a deviation is detected 815, the DMS server 320 determines 825 whether the deviation can be classified. In at least one embodiment, the DMS server 320 executes one or more models to analyze the detected deviation and classify the deviation. The one or more models are continuously updated to ensure that different types of deviations, including deviations of different materials, can be classified. Exemplary deviations include, but are not limited to, adhesion and / or accumulation of foreign matter such as ice, snow, dirt, dust, and / or oil, damage to one or more parts (components) of the machine, the need to calibrate one or more parts of the machine, missing parts of the machine, leaks, and / or other variations and / or deviations from the baseline of the machine and / or equipment. If the DMS server 320 cannot classify the deviation, the DMS server 320 prompts 830 the user for review. The DMS server 320 provides information to the user so that the user can determine the classification of the deviation. The user returns their review and the DMS server 320 determines whether the deviation is still detected. The user may review the classification information from the DMS server 320 and determine that no deviations exist. The DMS server 320 then determines 820 that no action is required and continues monitoring the process 800. In at least one embodiment, the user review also includes whether to activate the mitigation system 326 (shown in FIG. 3). If deviations are still detected after the user review, the DMS server 320 activates the mitigation system 326.

[0070] If the DMS server 320 classifies the deviation, the DMS server 320 measures 830 the amount of deviation present. In at least one embodiment, the DMS server 320 measures 830 the amount of deviation present using the pixel count and classification of the detected deviation. The DMS server 320 then compares 845 the amount of deviation present to one or more thresholds. In some embodiments, the thresholds are predefined by one or more users. In yet other embodiments, the thresholds are different for different types of deviations. The DMS server 320 determines 845 whether the deviation exceeds a high-risk threshold. If the deviation does not exceed a high-risk threshold, the DMS server 320 continues the monitoring process 800 by measuring 840 the deviation.

[0071] If the deviation exceeds the high-risk threshold or if the deviation is detected after user review, the DMS server 320 activates the mitigation system 326. In at least one embodiment, after activating the mitigation system 326, the DMS server 320 continues to measure the deviation 855. The DMS server 320 determines 860 whether the detected deviation still exceeds the low-risk threshold. If the deviation still exceeds the low-risk threshold, the DMS server 320 continues to use the mitigation system 326. If the deviation no longer exceeds the low-risk threshold, the DMS server 320 deactivates the mitigation system 326.

[0072] In an exemplary embodiment, the process 700 is continuously repeated by the DMS server 320 based on images received from the optical sensor 405. In some embodiments, the DMS server 320 analyzes multiple images substantially simultaneously in the decision process 800. In such embodiments, the DMS server 320 analyzes images transmitted from a sliding window of time. For example, the sliding window of time may be 30 seconds, 500 seconds, and / or any other window of time that enables the system to operate as described herein.

[0073] FIG. 9 shows a number of images 902 and corresponding deviation images 904 using systems 300 and 400 (shown in FIGS. 3 and 4). Images 902 may be provided by camera 316 (shown in FIG. 3) and / or optical sensor 405. For each of images 902, DMS server 320 (shown in FIG. 3) determines whether there are any detected deviations 715 (shown in FIG. 7). Deviation images 904 graphically show the detected deviations. DMS server 320 uses deviation images 904 to classify deviations 725 (shown in FIG. 7). Additionally, DMS server 320 counts the number of pixels in deviation images 904 to measure deviations 730 (shown in FIG. 7). Machine learning and more

[0074] The computer-implemented methods discussed herein include those discussed elsewhere herein and may include additional or alternative acts. The methods may be implemented via one or more local or remote processors, transceivers, servers, and / or sensors (such as processors, transceivers, servers, and / or sensors on board a vehicle or mobile device, or processors, transceivers, servers, and / or sensors associated with a smart infrastructure or a remote server), and / or via computer-executable instructions stored on a non-transitory computer-readable medium or media.

[0075] In some embodiments, the DMS server 320 is programmed to implement machine learning such that the DMS server 320 "learns" to analyze, organize, and / or process data without being explicitly programmed to do so. Machine learning may be implemented through machine learning methods and algorithms ("ML methods and algorithms"). In an exemplary embodiment, a machine learning module ("ML module") is configured to implement the ML methods and algorithms. In some embodiments, the ML methods and algorithms are applied to data inputs to generate machine learning outputs ("ML outputs"). The data inputs include, but are not limited to, images. The ML outputs may include, but are not limited to, identified objects, item classifications, material detection, material classifications, material quantification, and / or other data extracted from the images. In some embodiments, the data inputs may include a particular ML output.

[0076] In some embodiments, at least one of a number of ML methods and algorithms may be applied, including, but not limited to, linear or logistic regression, instance-based algorithms, regularization algorithms, decision trees, Bayesian networks, cluster analysis, association rule learning, artificial neural networks, deep learning, combined learning, reinforced learning, dimensionality reduction, and support vector machines. In various embodiments, the implemented ML methods and algorithms are directed toward at least one of a number of classifications of machine learning, such as supervised learning, unsupervised learning, and reinforcement learning.

[0077] In one embodiment, the ML module employs supervised learning. Supervised learning involves identifying patterns in existing data to make predictions about subsequent received data. Specifically, the ML module is "trained" with training data that includes example inputs and associated example outputs. Based on the training data, the ML module can generate a predictive function that maps outputs to inputs and utilize the predictive function to generate ML outputs based on the data inputs. Exemplary inputs and exemplary outputs of the training data may include any of the data inputs or ML outputs described above. In an exemplary embodiment, the processing element may be trained by providing a large sample of images with known characteristics or features. Such information may include, for example, information related to multiple images of multiple different objects, items, and / or characteristics.

[0078] In another embodiment, the ML module can employ unsupervised learning, which involves finding meaningful relationships from unorganized data. Unlike supervised learning, unsupervised learning does not involve user-initiated learning based on example inputs with associated outputs. Rather, in unsupervised learning, the ML module can organize unlabeled data according to relationships determined by at least one ML technique / algorithm employed by the ML module. The unorganized data can include any combination of data inputs and / or ML outputs, as described above.

[0079] In yet another embodiment, the ML module may employ reinforcement learning, including optimizing an output based on feedback from a reward signal. Specifically, the ML module may receive a user-defined reward signal definition, receive a data input, utilize a decision-making model to generate an ML output based on the data input, receive a reward signal based on the reward signal definition and the ML output, and subsequently modify the decision-making model to receive a stronger reward signal for the generated ML output. Other types of machine learning, such as deep or combined learning techniques, may also be employed.

[0080] Based on these analyses, the processing element can learn how to identify characteristics and patterns that can then be applied to analyze images of components to identify deviations from the baseline. The processing element may also learn how to identify attributes of different objects in different lighting and / or other conditions. This information can be used to determine which classification model to use and which classification to provide. Other considerations

[0081] As will be understood based on the foregoing specification, the above-described embodiments of the present disclosure may be implemented using computer programming or engineering techniques, including computer software, firmware, hardware, or any combination or subset thereof. Such resulting programs having computer readable code means may be embodied or provided in one or more computer readable media, thereby creating a computer program product, i.e., an article of manufacture, according to the discussed embodiments of the present disclosure. The computer readable medium may be, for example, but not limited to, a fixed (hard) drive, a diskette, an optical disk, a magnetic tape, a semiconductor memory such as a read-only memory (ROM), and / or any transmission / reception medium, such as the Internet or other communications network or link. An article of manufacture containing the computer code may be produced and / or used by executing the code directly from one medium, by copying the code from one medium to another, or by transmitting the code over a network.

[0082] These computer programs (also known as programs, software, software applications, "apps," or code) contain machine instructions for a programmable processor and may be implemented in high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., magnetic disk, optical disk, memory, programmable logic device (PLD)) used to provide machine instructions and / or data to a programmable processor, including a machine-readable medium that receives the machine instructions as a machine-readable signal. However, the terms "machine-readable medium" and "computer-readable medium" do not include transitory signals. The term "machine-readable signal" refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0083] As used herein, the term "database" can refer to either a body of data, a relational database management system (RDBMS), or both. As used herein, a database can include any collection of data including hierarchical databases, relational databases, flat file databases, object-relational databases, object-oriented databases, and any other structured collection of records or data stored in a computer system. The above examples are illustrative and do not limit the definition or meaning of the term database. Examples of RDBMS include, but are not limited to, Oracle® Database, MySQL®, IBM® DB2, Microsoft® SQL Server, Sybase®, PostgreSQL, and the like. However, any database that enables the systems and methods described herein can be used. (Oracle is a registered trademark of Oracle Corporation, Redwood Shores, California; IBM is a registered trademark of International Business Machines Corporation, Armonk, New York; Microsoft is a registered trademark of Microsoft Corporation, Redmond, Washington; and Sybase is a registered trademark of Sybase, Dublin, California.)

[0084] As used herein, a processor may include any programmable system, including systems that use microcontrollers, reduced instruction set circuits (RISC), application specific integrated circuits (ASICs), logic circuits, and other circuits or processors capable of performing the functions described herein. The above examples are illustrative, and thus are not intended to limit the definition and / or meaning of the term "processor."

[0085] As used herein, the terms "software" and "firmware" are used interchangeably and include any computer program stored in memory for execution by a processor, including RAM memory, ROM memory, EPROM memory, EEPROM memory, and non-volatile RAM (NVRAM) memory. The above memory types are exemplary and are not intended to limit the types of memory that may be used to store computer programs.

[0086] In another embodiment, a computer program is provided, the program being embodied in a computer readable medium. In one example, the system runs on a single computer system without requiring connection to a server computer. In yet another example, the system runs in a Windows environment (Windows is a registered trademark of Microsoft Corporation, Redmond, Washington). In yet another example, the system runs in a mainframe environment and a UNIX server environment (UNIX is a registered trademark of X / Open Company Limited, Reading, Berkshire, England). In a further example, the system runs in an iOS environment (iOS is a registered trademark of Cisco Systems, Inc., San Jose, California). MacOS is a registered trademark of Apple Inc., Cupertino, California. In yet another example, the system runs on an Android OS (Android is a registered trademark of Google, Inc., Mountain View, California). In another example, the system runs on a Linux OS (Linux is a registered trademark of Linus Torvalds, Boston, Massachusetts). The application is flexible and designed to run in a variety of different environments without compromising its primary functionality.

[0087] In some embodiments, the system includes multiple components distributed among multiple computing devices. One or more components may be in the form of computer-executable instructions embodied in a computer-readable medium. The systems and processes are not limited to the specific embodiments described herein. In addition, each system and process component can be implemented separately, independent of other components and processes described herein. Each component and process can also be used in combination with other assembly packages and processes.

[0088] As used herein, elements or steps described in the singular and preceded by the word "a" or "an" should be understood not to exclude a plurality of elements or steps, unless such exclusion is expressly recited. Moreover, references to an "embodiment" or "one embodiment" of the present disclosure are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features. Furthermore, to the extent that the terms "includes," "including," "has," "contains," and variations thereof are used herein, such terms are intended to be inclusive in a similar manner to the term "comprises" as an open-ended transition term, without excluding any additional or other elements.

[0089] Additionally, as used herein, the term "real-time" refers to at least one of the time of occurrence of relevant events, the time of measurement and collection of given data, the time of processing the data, and the time of the system's response to events and environments. In the examples described herein, these activities and events occur substantially instantaneously.

[0090] The patent claims at the end of this document are not intended to be construed under 35 USC § 112(f) unless traditional means-plus-function language is expressly recited in the claim, such as by expressly recited "means for" or "step for" language.

[0091] This specification uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems, and performing the incorporated methods. The patentable scope of the invention is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements that do not differ insubstantially from the literal language of the claims.

[0092] Further aspects of the invention are provided by the subject matter of the following clauses. [Embodiment 1] A system for monitoring at least one component using at least one processor in communication with at least one memory device, the at least one processor being programmed to perform the steps of storing a plurality of baseline information relating to at least one component being monitored, receiving a plurality of current images of the at least one component being monitored, detecting a deviation from a baseline based on a comparison of the plurality of current images with the plurality of baseline information, classifying the deviation, and implementing corrective action based on the classification of the deviation. [Embodiment 2] The system described in embodiment 1, wherein the at least one processor is further programmed to determine an amount of deviation based on the comparison and determine which of a plurality of corrective actions to implement. [Embodiment 3] The system described in embodiment 1 or 2, wherein the at least one processor is further programmed to notify a user via a client device that corrective action should be initiated, receive a response from the user, and initiate corrective action based on the user's response. [Embodiment 4] A system described in any of embodiments 1 to 3, wherein the at least one processor is further programmed to activate at least one mitigation system when the detected deviation is determined to be ice accumulation and the determined amount of accumulation exceeds a predetermined threshold. [Embodiment 5] A system described in any of embodiments 1 to 4, wherein the at least one processor is further programmed to monitor the amount of deviation detected during operation of the at least one mitigation system, and to deactivate the at least one mitigation system when the amount of deviation detected no longer exceeds a predetermined threshold. [Embodiment 6] A system described in any of embodiments 1 to 5, wherein the at least one processor is further programmed to receive additional sensor information from one or more sensors monitoring the at least one component. [Embodiment 7] A system described in any of embodiments 1 to 6, wherein the at least one processor is further programmed to determine whether conditions are appropriate for a deviation based on additional sensor information received. [Embodiment 8] A system described in any of embodiments 1 to 7, wherein the at least one processor is further programmed to classify the deviation based on additional sensor information received. [Embodiment 9] A system described in any of embodiments 1 to 8, wherein the at least one processor is further programmed to classify the deviation according to the cause of the deviation. [Embodiment 10] A system described in any of embodiments 1 to 9, wherein the deviation is an adhesion of a substance and the deviation is classified by the substance. [Embodiment 11] A system described in any of embodiments 1 to 10, wherein the at least one component is part of a turbine and the multiple current images are transmitted from a camera looking at the compressor through a window defined in an inlet plenum. [Embodiment 12] A method for monitoring at least one component in an apparatus, the method being implemented on a computer system including at least one processor in communication with at least one memory device, the method including the steps of storing a plurality of baseline information relating to at least one component being monitored, receiving a plurality of current images of the at least one component being monitored, detecting a deviation from a baseline based on the plurality of current images and the plurality of baseline information, classifying the deviation, and implementing corrective action based on the classification of the deviation. [Embodiment 13] The method of embodiment 12, further comprising a step of determining an amount of deviation based on the comparison, and a step of determining which of a plurality of corrective actions should be implemented. [Embodiment 14] A method as described in embodiment 12 or 13, further comprising the steps of notifying a user via the client device that corrective action should be initiated, receiving a response from the user, and initiating the corrective action based on the response. [Embodiment 15] A method as described in any of embodiments 12 to 14, further comprising a step of activating at least one mitigation system when the detected deviation is determined to be ice accumulation and the determined amount of accumulation exceeds a predetermined threshold. [Embodiment 16] A method described in any of embodiments 12 to 15, further comprising a step of monitoring the amount of deviation detected during operation of the at least one mitigation system, and a step of deactivating the at least one mitigation system when the amount of deviation detected no longer exceeds a predetermined threshold. [Embodiment 17] A method according to any of embodiments 12 to 16, further comprising a step of receiving additional sensor information from one or more sensors monitoring the device. [Embodiment 18] A method according to any of embodiments 12 to 17, further comprising a step of determining whether conditions are appropriate for a deviation based on received additional sensor information. [Embodiment 19] A method according to any of embodiments 12 to 18, further comprising a step of classifying the deviation based on received additional sensor information. [Embodiment 20] A method according to any of embodiments 12 to 19, wherein the apparatus is a turbine and multiple current images are transmitted from a camera viewing the compressor through a window defined in the inlet plenum.

[0093] While the invention has been described in various specific embodiments, those skilled in the art will recognize that the invention can be practiced with modification within the spirit and scope of the appended claims. [Explanation of symbols]

[0094] 100: Gas turbine engine 102: Intake section 104: Compressor section 106: Compressor casing 108: Combustor section 110: Turbine section 112: Turbine casing 114: Exhaust section 116: Rotor assembly 118: Compressor rotor / drive shaft 120: Turbine rotor / drive shaft 122: Combustor 124: Fuel nozzle assembly 126: Load 128: Blade assembly 130: Stationary vane assembly 132: Rotating turbine blade assembly / turbine bucket 134: Stationary nozzle assembly 136: Intake air 138, 140: Compressed air 142: Combustion gases 144: Exhaust gases 150: Jet turbine engine 152: Fan assembly 154: Booster 156: High pressure compressor 158: Combustor 160: High pressure turbine 162: Low pressure turbine 164: Fan blades 166: Rotor disk 168: Intake side 170: Exhaust side 172: First rotor shaft 174: Second rotor shaft 300, 400: System 302: Intake system 304: Air duct 306: Inlet silencer 308: Heat manifold / piccolo pipe 310: Gas turbine inlet plenum 312: Window 314: Lighting system 316: Camera 318: Device sensor 320: Deviation monitoring system (DMS) computing device 322: Data 324: Turbine control system 326: Mitigation system 328: Client device 405: Light sensor 410: Device control system 415: Database server 420: Database 501: User 502: User computing device 505, 605: Processor 510, 610: Memory area 515: Media output component 520: Input device 525, 615: communication interface 602: server computer device 620: storage interface 625: storage device 902: image 904: deviation image

Claims

1. 1. A system for monitoring at least one component using at least one processor in communication with at least one memory device, comprising: The at least one processor storing a plurality of baseline information associated with at least one component being monitored; receiving a plurality of current images of the at least one component being monitored; detecting deviations from a baseline based on a comparison of the plurality of current images to the plurality of baseline information; Categorizing the deviations; implementing corrective action based on said classification of said deviation; The system is programmed to do so.

2. The at least one processor further comprises: determining an amount of deviation based on the comparison; The system of claim 1 , programmed to determine which of a plurality of corrective actions to implement.

3. The at least one processor further comprises: notifying the user, via the client device, that corrective action should be initiated; receiving a response from the user; activating a corrective action based on the response of the user. The system of claim 1 , programmed to:

4. The at least one processor further comprises: The deviation detected is determined to be ice accumulation, the determined accumulation amount exceeds a predetermined threshold; 10. The system of claim 1, further comprising:

5. The at least one processor further comprises: monitoring the amount of deviation detected during operation of the at least one mitigation system; deactivating at least one mitigation system when the detected amount of deviation no longer exceeds a predetermined threshold.

5. The system of claim 4, programmed to:

6. The at least one processor further comprises: The system of claim 1 , further programmed to receive additional sensor information from one or more sensors monitoring the at least one component.

7. The at least one processor further comprises: The system of claim 6 , further programmed to determine whether conditions are appropriate for the deviation based on the received additional sensor information.

8. The system of claim 6 , wherein the at least one processor is further programmed to classify the deviation based on received additional sensor information.

9. The system of claim 1 , wherein the at least one processor is further programmed to classify the deviations according to a cause of the deviations.

10. The system of claim 1 , wherein the deviations are material deposits, and the deviations are classified by material.

11. The system of claim 1 , wherein the at least one component is part of a turbine, and the plurality of current images are transmitted from a camera viewing the compressor through a window defined in the inlet plenum.

12. 1. A method for monitoring at least one component in a device, the method being implemented on a computer system including at least one processor in communication with at least one memory device, the method comprising: storing a plurality of baseline information associated with the at least one monitored component; receiving a plurality of current images of the at least one component being monitored; detecting a deviation from a baseline based on the plurality of current images and the plurality of baseline information; classifying said deviations; taking corrective action based on said classification of deviations; A method comprising:

13. determining an amount of the deviation based on the comparison; and determining which of a plurality of corrective actions to implement; The method of claim 12, comprising:

14. notifying a user via a client device that corrective action should be initiated; receiving a response from the user; initiating corrective action based on the response; The method of claim 12, comprising:

15. The detected deviation is determined to be ice accumulation; the determined accumulation exceeds a predetermined threshold; 13. The method of claim 12, further comprising activating at least one mitigation system if

Images