Patent Description:
Power generation system is a system that converts primary sources of energy into a secondary energy source, the electricity. The primary sources of energy may include fossil fuels (such as coal, crude oil, and natural gas), hydraulic forces (such as running water from a dam), nuclear reaction, wind, solar and geothermal energy, and so on. In many regions, a large portion of electricity is generated from electric power plant that use turbines or similar machines to drive electric generators. A turbine generator system uses a moving fluid (such as water, steam, combustion gases, or air) to pushes a series of blades mounted on a shaft, which rotate the shaft connected to an electric generator. The electric generator, in turn, converts the kinetic energy into electrical energy based on the relationship between magnetism and electricity. Different types of turbines include steam turbines, combustion (gas) turbines, water (hydroelectric) turbines, and wind turbines.

<CIT> describes a method for controlling noise from a wind park that has a plurality of wind turbines includes monitoring noise emission from the wind turbines in at least a near field area and utilizing a transfer function of noise emission to determine a noise impact importance of the wind turbines at one or more locations in a far field area beyond a boundary of the wind park. The method further includes determining which, if any, wind turbines to operate in a noise-reduced operation mode in accordance with the noise impact importance determination and controlling operation modes of the wind turbines in accordance with the determination of which, if any, wind turbines to operate in a noise reduced mode.

GRAHAM L J ET AL: "ACOUSTIC EMISSION MONITORING OF STEAM TURBINE: A REVIEW OF PROGRESS" concerns the acoustic emission monitoring in steam turbines for detecting cracking of the turbine shaft, wherein the transducers should be placed inside the rotor bore hole, viz. as close to the location of the cracking and as far from extraneous noise source as possible. However, this location is a high temperature environment increasing the effort for reliability and powering of the sensor.

During the operations of a turbine generator system, excessive noises may be generated from various moving objects (such as mechanical parts and fluids) or other physical events (such as resonances). Some noises may cause environmental problems, for example, complaints from the neighbors. Some noises may indicate potential problems that may lead to system/component failure, if not being addressed timely or properly. Therefore, noises generated from the turbine generator system need to be monitored closely.

The objective of the invention is solved by the system according to claim <NUM> and the method according to claim <NUM>.

In a first embodiment, a system is provided. The system includes an acoustic monitoring, analysis, and diagnostic system that includes a processor. The processor receives NF noise signals from a near field (NF) microphone array that measures noises generated from a power generation system in a near field, and also receives FF noise signals from a far field (FF) microphone array that measures noises generated from a power generation system in a far field. Based on the received signals, the processor derives NF noise measurements and FF noise measurements. The processor also synchronizes the NF noise measurements and the FF noise measurements to create synchronized NF noise data and synchronized FF noise data, which are analyzed by the processor to create a NF noise signature and an FF noise signature. Based on the NF noise signature and FF noise signature, the processor diagnoses one or more root causes of noises generated from the power generation system and reports the one or more root causes of the noises generated from the power generation system.

In a second embodiment, a method is provided. In accordance with this method, an acoustic monitoring, analysis, and diagnostic system measures noises generated from a power generation system and traveling in the near field via a near field (NF) microphone array and receives NF noise signals from the NF microphone array. The acoustic monitoring, analysis, and diagnostic system also measures noises generated from the power generation system and traveling in the far field via a far field (FF) microphone array and receives FF noise signals from the FF microphone array. Based on the NF signals and the FF signals, the acoustic monitoring, analysis, and diagnostic system derives NF noise measurements and FF noise measurements. The acoustic monitoring, analysis, and diagnostic system synchronizes the NF noise measurements and the FF noise measurements into synchronized NF noise data and synchronized FF noise data. Based on the NF noise measurements and FF noise measurements, the acoustic monitoring, analysis, and diagnostic system monitors noise performance of the power generation system. The acoustic monitoring, analysis, and diagnostic system also analyzes the synchronized NF noise data and the synchronized FF noise data to create NF noise signature and FF noise signature, based on which the acoustic monitoring, analysis, and diagnostic system diagnoses root causes of the measured noises generated from the power generation system. Further, the acoustic monitoring, analysis, and diagnostic system controls the NF microphone array and FF microphone array to measure the noise continuously to generate continuous recorded acoustic signals that provide continuous monitoring of collected data to recognize a change for early failure detection based on the analysis of historical data over the life time of the monitored power generation system.

In a third embodiment, a non-transitory, computer-readable medium storing instructions is provided. The instructions, when executed by one or more processors, cause the one or more processors to control a near field (NF) microphone array to measure noises generated from a power generation system, and to receive NF noise measurements from the NF microphone array. The instructions also cause the one or more processors to control a far field (FF) microphone array to measure noises generated from the power generation system, and to receive FF noise measurements from the FF microphone array. The instructions also cause the one or more processors to synchronize the NF noise measurements and FF noise measurements to create synchronized NF noise data and synchronized FF noise data. The instructions also cause the one or more processors to monitor noise performance of the power generation system based on the measured noises from the NF microphone array and FF microphone array. The instructions also cause the one or more processors to analyze the synchronized NF noise data and synchronized FF noise data to create NF noise signature and FF noise signature. The instructions also cause the one or more processors to diagnose root causes of the measured noises generated from the power generation system based on the NF noise signature and FF noise signature. The instructions further cause the one or more processors to control the near field (NF) microphone array and far field (FF) microphone array to measure the noise continuously to generate continuous recorded acoustic signals that enable continuous monitoring of collected data to recognize change(s) for early failure detection based on the analysis of historical data over the life time of the monitored power generation system.

Furthermore, any numerical examples in the following discussion are intended to be nonlimiting, and thus additional numerical values, ranges, and percentages are within the scope of the disclosed embodiments.

Power generation systems may be used to convert different energy sources into electrical power. The energy sources may be hydrocarbonaceous, coal, natural gas, nuclear, solar, wind energy, and so on. In some power production systems, gas turbine systems may combust hydrocarbonaceous fuel for the generation of electricity. The gas turbine systems may include compressor(s), a combustor, gas turbine(s), and an electric generator. Gas turbines are engines used for producing rotary movement to turn electric generators. The gas turbines may combust natural gas or other hydrocarbonaceous fuels to produce mechanical motion, which may then be used to drive the electric generators to produce electrical energy. More specifically, the gas turbines combust a mixture of air and fuel in combustors, where the combustions of the air-fuel mixture create hot pressurized gases that may cause turbine blades to spin a shaft coupling the gas turbine engine to one or more electrical generators, thus driving the generators which in turn convert rotational motion into electricity. Some gas turbine systems may generate unwanted noises. For example, high noise levels may indicate issues that may lead to unwanted maintenance. But the causes of noises may not be easy to identify.

During operations, operators of the power generation system may benefit from quick response and systems for failure prediction based on noise detections. Automated systems and operators may also benefit from monitoring a power system performance at various operating conditions locally (e.g., from an onsite control room), and/or remotely (e.g., from a network or cloud). In certain operations, the automated systems and/or operators may check for noise performance from both near field by using sensors deployed close to the power generation system, and from far field by using sensors deployed far from the power generation system. For example, the automated systems and/or operators may benefit from far field observations that minimize environmental noise pollution.

The techniques disclosed herein includes a cloud-based acoustic monitoring, analysis, and diagnostic (CAMAD) system, which may additionally include hardware and/or software used to monitor remotely noise performance of a power generation system in both near field and far field. The CAMAD system may provide root cause analysis using system and component noise signatures at both near field and far field. Besides convenient access at various locations (locally or remotely), the CAMAD system may be used to detect problems early to prevent costly repair, to predict component failure and to avoid service interruption.

Turning now to the drawings, <FIG> is a block diagram depicting an embodiment of a machine that may be controlled via a control system. In the depicted embodiment, a machine <NUM> is operatively coupled to a control system <NUM> so that the machine <NUM> may be controlled by the control system <NUM> to perform instructed operations. One or more sensors <NUM> may be deployed to monitor operational performance of the machine <NUM>, of ambient conditions, of related systems, and so on, to provide information to the control system <NUM>, which may then further analyze and process the received information and create control signal(s) to the machine <NUM> to perform intended operation(s), for example via actuators such as valves, fuel throttles, pumps, positioners, and so on.

The machine <NUM> may be a mechanical system that may use and/or produce power to apply forces and to control movement based on the instructed operations. During operations, the machine <NUM> may generate certain physical events or changes <NUM>, such as sound, motion (e.g., vibration or displacement), heat, moisture, pressure, electromagnetic field, light or chemical substance, etc. The physical events or changes <NUM> may be detected by the sensor(s) <NUM>. The output from the sensor <NUM> may be one or more signals <NUM>, which may be used by the control system <NUM> and/or converted to be displayed by a human-readable display. The sensor(s) <NUM> may be a device, module, or sensor system that may detect and/or respond to various changes in the physical environment and/or the machine <NUM>. For example, the sensor(s) <NUM> may be acoustic sensors, motion sensors, thermal sensors, pressure sensors, radio-frequency sensors, optical sensors, chemical sensors (e.g., ozone sensors), and the like.

The control system <NUM> may include one or more processors <NUM> that may receive the signals <NUM> from the sensor(s) <NUM>. If the signals <NUM> include analog signals, an analog-to-digital converter may be used to convert the analog signal(s) to digital signal(s) that may then be further used by the processor(s) <NUM>. The processor(s) <NUM> may analyze and process the receive signal(s), and output control signal(s) <NUM> based on results from data analysis and processing. The control system <NUM> may also include memory device(s) <NUM> to store data including computer code or instructions that may execute various processes related to signal analysis and processing. The memory devices(s) <NUM> may include random access memory (RAM), read only memory (ROM), storage devices (e.g., hard drives, USB sticks), and/or storage systems (e.g., relational databases, non-relational databases). The control system <NUM> may further include monitoring and alarming/warning systems, including human machine interface (HMI) systems, displays, audio systems, and so on, that may that enable users to enter inputs into the control system <NUM> and to monitor the operational performance of the machine <NUM>. The control system <NUM> may be a local control system (e.g., located in an onsite control room), a network-based control system, cloud-based systems, or a combination thereof.

<FIG> is a block diagram depicting an embodiment of a power production system <NUM> (e.g., machine <NUM>) which is depicted as including a turbine system, sensor(s) (e.g., sensor(s) <NUM>), and a turbine control system (e.g., control system <NUM>) as described with respect to <FIG>. A turbine system <NUM> includes two gas turbine engines <NUM> and <NUM>, which may use liquid or gas fuel, such as natural gas and/or a hydrogen rich synthetic gas, to run the turbine system <NUM>. For instance, fuel nozzles may spray a fuel supply, mix the fuel with an oxidant (e.g., air), and distribute the oxidant-fuel mixture into a combustor <NUM>. The combustion of the oxidant-fuel mixture may create hot pressurized gases within the combustor <NUM>, which may be directed through a turbine section <NUM> that includes a high-pressure (HP) turbine engine <NUM> and a low-pressure (LP) turbine engine <NUM>, and towards an exhaust <NUM>. In the illustrated embodiment, the HP turbine engine <NUM> engine may be part of a HP rotor section, and the LP turbine engine <NUM> may be part of a LP rotor section of the turbine section <NUM>. As the exhaust gases pass through the HP turbine engine <NUM> and LP turbine engine <NUM>, the gases may force turbine blades to rotate a drive shaft <NUM> extending along a rotational axis. As illustrated, drive shaft <NUM> is connected to various components of the turbine system <NUM>, including a HP compressor <NUM> and a LP compressor <NUM>.

The drive shaft <NUM> of the turbine system <NUM> may include one or more shafts that may be, for example, concentrically aligned. The drive shaft <NUM> may include a shaft connecting the HP turbine engine <NUM> to the high-pressure compressor <NUM> of a compressor section <NUM> of the turbine system <NUM> to form a HP rotor. For example, the HP compressor <NUM> may include compressor blades coupled to the drive shaft <NUM>. Thus, rotation of turbine blades in the HP turbine engine <NUM> may cause the shaft connecting the HP turbine engine <NUM> to the HP compressor <NUM> to rotate the compressor blades within the HP compressor <NUM>, which compresses air in the HP compressor <NUM>. Similarly, the drive shaft <NUM> may include a shaft connecting the LP turbine engine <NUM> to a low-pressure compressor <NUM> of the compressor section <NUM> to form a LP rotor. Thus, in the illustrated embodiment, the drive shaft <NUM> may include both an HP and an LP rotor for driving the HP compressor/turbine components and the LP compressor/turbine components, respectively. The LP compressor <NUM> may include compressor blades coupled to the drive shaft <NUM>. Thus, rotation of turbine blades in the LP turbine engine <NUM> causes the shaft connecting the LP turbine <NUM> to the LP compressor <NUM> to rotate compressor blades within the LP compressor <NUM>.

The rotation of compressor blades in the HP compressor <NUM> and the LP compressor <NUM> may act to compress air that is received via an air intake <NUM>. As shown in <FIG>, the compressed air is fed to the combustor <NUM> and mixed with fuel to allow for higher efficiency combustion. Thus, the turbine system <NUM> may include a dual concentric shafting arrangement, wherein LP turbine engine <NUM> is drivingly connected to LP compressor <NUM> by a first shaft of the drive shaft <NUM>, which the HP turbine engine <NUM> is similarly drivingly connected to the HP compressor <NUM> by a second shaft in the drive shaft <NUM>, which may be disposed internally and in a concentric arrangement with respect to the first shaft. In the illustrated embodiment, the shaft <NUM> may also be connected to load <NUM>, which may include any suitable device that is powered by the rotational output of turbine system <NUM>. For example, the load <NUM> could include a vehicle or a stationary load, such as an electric generator in a power plant or a propeller on an aircraft. In some embodiments, the turbine system <NUM> may be an aeroderivative gas turbine used in marine propulsion, industrial power generation, and/or marine power generation applications. Further, it should be noted that while the turbine system depicted in <FIG> is a representation of a cold-end system (e.g., the load <NUM> is disposed upstream from the intake with respect to the air flow direction), other embodiments may also include hot-end systems (e.g., with the load <NUM> being disposed downstream from the exhaust <NUM> with respect to the air flow direction).

To provide turbine performance information to a turbine control system <NUM>, the gas turbine system <NUM> may include a set of sensors <NUM>, wherein the sensors <NUM> are configured to monitor various turbine engine parameters related to the operation and performance of the turbine system <NUM>. The sensors <NUM> may include, for example, one or more inlet sensors and outlet sensors positioned adjacent to, for example, the inlet and outlet portions of the HP turbine engine <NUM>, the LP turbine <NUM>, the HP compressor <NUM>, the LP compressor <NUM>, and/or the combustor <NUM>, as well as the intake <NUM>, the exhaust <NUM>, and/or the load <NUM>. Further, the sensors <NUM> may include measured and/or virtual sensors. As can be appreciated, a measured sensor may refer to a physical sensor (e.g., hardware) that is configured to acquire a measurement of a particular parameter(s), whereas a virtual sensor may be utilized to obtain an estimation of a parameter of interest and may be implemented using software. In some embodiments, virtual sensors may be configured to provide estimated values of a parameter that is difficult to directly measure using a physical sensor.

By way of example, these various inlet and outlet sensors <NUM>, which may include measured and virtual sensors, may sense parameters related to environmental conditions, such as ambient temperature and pressure and relative humidity, as well as various engine parameters related to the operation and performance of the turbine system <NUM>, such as compressor speed ratio, inlet differential pressure, exhaust differential pressure, inlet guide vane position, fuel temperature, generator power factor, water injection rate, compressor bleed flow rate, exhaust gas temperature and pressure, compressor discharge temperature and pressure, generator output, rotor speeds, turbine engine temperature and pressure, fuel flow rate, core speed. The sensors <NUM> may also be configured to monitor engine parameters related to various operational phases of the turbine system <NUM>.

The measurements <NUM> of turbine system parameters obtained by the sensors <NUM> may be provided to the turbine control system <NUM>, which is configured to perform monitoring, analysis, diagnostic, and regulating tasks on the turbine system <NUM>. The turbine control system <NUM> may use local and/or cloud based processor(s) and/or memory (e.g., processor(s) <NUM>, memory <NUM>) and predetermined routines (stored in computer-readable medium) to process and analyze the measurements <NUM> received, run diagnose, and generate control signals <NUM> based on analytic and diagnostic results. The control signals <NUM> are sent to the corresponding components of the turbine engine <NUM> and the load <NUM> for performing new tasks.

The turbine control system <NUM> may include a local control system (e.g., located inside a control room close to the turbine system <NUM>). For example, the local control system, either directed by an operator or operating in an automatic mode, may adjust actuators within the turbine system <NUM> to regulate the function of the turbine system <NUM> by changing parameters such as fuel flow rate, vane angle, and nozzle area. The actuators may include mechanical, hydraulic, pneumatic, or electromagnetic actuators that manage the movement of valves controlling air and fuel flow within air and fuel flow paths of the turbine system <NUM>.

The turbine control system <NUM> may also include a cloud-based (or network-based) monitoring, analysis, and diagnostic system, which will be detailed below with respect to <FIG>. The cloud-based monitoring, analysis, and diagnostic system may use the measurements <NUM> obtained by the sensors <NUM> to monitor remotely the performance of the turbine system <NUM>, to run data analysis and simulation(s) to predict component failure and avoid service interruption, and to perform intelligent diagnostics for troubleshooting. The cloud-based monitoring, analysis, and diagnostic system enables convenient and remote access to the turbine system <NUM>. For example, the turbine system <NUM> may be a gas turbine generator system (GTG) installed on a remote and isolated offshore oil rig. By using the cloud-based monitoring, analysis, and diagnostic system, the operator may remotely manage, direct, and regulate GTG operations without being physically proximity to the GTG.

<FIG> illustrates schematic diagram of an embodiment of an acoustic monitoring, analysis, and diagnostic system, which in some embodiments, may be a cloud-based acoustic monitoring, analysis, and diagnostic (CAMAD) system <NUM> that may be used by the power generation system <NUM> that may include the gas turbine system <NUM> of <FIG>. The power generation system <NUM> may include the turbine system <NUM> and an electric generator (e.g., load <NUM>). During operations, the power generation system <NUM> may generate certain noises <NUM> (e.g., disordered sound waves), which may be detected by microphones <NUM> (acoustic sensors for converting sound waves into electrical signals) deployed in predetermined positions. Recorded acoustic signals <NUM> may then be routed to the turbine control system <NUM>, which may include the CAMAD system <NUM> and a local control system <NUM>. In other embodiments, the CAMAD system <NUM> may be a system separate from the control system <NUM> but communicatively and/or operatively coupled to the control system <NUM>. The recorded acoustic signals <NUM> may be calculated, processed, and analyzed by the CAMAD system <NUM>. The processed/analyzed data information may be used to predict and detect unwanted events that may call for planning maintenance, repair, and/or other services to improve operational uptime and minimize downtime, thus improving productivity. Based on the processed/analyzed data information, the turbine control system <NUM> may generate the control signals <NUM> and send the control signals <NUM> to the power generation system <NUM> for performing new tasks. For example, one of the control signals <NUM> may instruct the intake <NUM> to adjust the air supply rate, fuel, flame characteristics, and so on, of the air-fuel mixture combusted by the combustor <NUM>.

The noise <NUM> may be generated from the turbine system <NUM> (e.g., fans or pumps), the generator (e.g., motors), or supplementary parts/equipment (e.g., connecting pipes or turbine enclosure) or any subsystem of the power production system <NUM>. In some circumstances, the source of the noise <NUM> may be easily identified. For example, the noise <NUM> may be caused by instabilities observed when a mechanical part is vibrating or otherwise shaking in an undesired manner. In some other circumstances, the source of the noise <NUM> may be more complex. For example, the noise <NUM> may be caused by a structural or acoustic resonance condition, such as an acoustic resonance, a vibration resonance, and/or turbulence.

As illustrated, the noise <NUM> may be captured by the microphones <NUM>. Microphones may be used as an acoustic wave sensor that detects audio by converting sound waves into electrical signals. The microphones <NUM> depicted may be used to capture acoustic waves (i.e., noise <NUM>) from the components of the power generation system <NUM> at certain locations and may convert the captured acoustic waves into the recorded acoustic signals <NUM>. The positions of microphones <NUM> may be determined, for example, by engineers and/or operators who install, operate, and maintain the power generation system <NUM>. Positioning the microphones <NUM> may include determining more critical and/or "noisy" components of the power generation system <NUM> that need monitoring over the time and determining appropriate locations to deploy the microphones <NUM> to monitor noise performance for the respective components.

After detecting and converting the noise <NUM> into the recorded acoustic signals <NUM>, the microphones <NUM> and/or related recording equipment may send the recorded acoustic signals <NUM> to the CAMAD <NUM>. The CAMAD <NUM> may include hardware and software systems used to monitor remotely the noise performance of the power generation system <NUM>. The CAMAD <NUM> may include data (signal) processing related components, such as an acoustic monitoring and analysis module <NUM>, an intelligent diagnostic module <NUM>, and one or more processors <NUM>. The CAMAD <NUM> may also include storage related components, such as one or more databases <NUM> and memory <NUM>. In addition, the CAMAD <NUM> may include a user interface, such as a Human Machine Interface (HMI) <NUM> to facilitate operational controls.

The acoustic monitoring and analysis module <NUM> may collect, process, and analyze the recorded acoustic signals <NUM> via one or more processors <NUM>. The processed and analyzed data may be used to monitor the noise performance of the power generation system <NUM>, and to provide early failure detection and preparation for services or replacements to eliminate operation downtime and increase productivity. The data processing and analysis may be executed by using pre-determined routines stored in the memory <NUM> (e.g., computer programs). The collected and processed data may be categorized, tagged, and stored into one or more databases <NUM>. For example, data tags may be used as identifications by one or more processors <NUM> to cause the data to be stored to an appropriate location in one or more databases <NUM>.

The data processing and analysis performed by the acoustic monitoring and analysis module <NUM> may include using different filtering techniques to remove unwanted noise(s) (such as background noise or ambient noise), thus increasing the possibilities of detecting the concerned noise(s). The filtering may be conducted in a time (e.g., using a random noise filter, a finite impulse response filter, or an adaptive filter) and a frequency domain (e.g., using a band-pass filter or a harmonic filter). The data processing and analysis may also include using the fast Fourier transform (FFT), which may convert a signal from its original domain (e.g., time) to a representation in the frequency domain and vice versa. Fourier analysis may provide system and component noise signature of the power generation system <NUM>, which may be used for instant root cause analysis. The data processing and analysis may further include other audio signal processing techniques. For example, active noise control may be used to reduce unwanted sound. By creating a signal that is representative, and in some cases, identical to the unwanted noise but with the opposite polarity, the two signals may cancel out due to destructive interference.

Based on the_recorded acoustic signals <NUM> and the processed data output from the acoustic monitoring and analysis module <NUM>, the intelligent diagnostic module <NUM> may predict future system/component failure and avoid service interruption caused by such failures. The prediction of system/component failure may use computer simulations approximately imitating the operation of the power generation system <NUM> or the components of the power generation system <NUM> (e.g., the combustor <NUM>, HP turbine engine <NUM>, or exhaust <NUM>) over time. For example, thermodynamic models, finite element analysis models (FEA), Computational Fluid Dynamics (CFD), chemical models, combustion models, and so on, may be used that model behavior for the power generation system <NUM> and resultant noise. The computer simulations may provide different noise levels and/or sound patterns by using the_recorded acoustic signals <NUM> and/or synthetic signals under different operational circumstances (e.g., turbine ramp up, turbine baseload, turbine shutdown). For example, the different noise levels may be created during computer simulations, including noise levels in low load operation mode and high load operation mode.

To provide the early failure detection and root cause analysis, the sources of the noise are to be identified via the CAMAD <NUM>. Identifying the sources of the noise <NUM> may include conducting noise measurements during controlled testing runs of power generation system <NUM> (e.g., during initial installation tests). The noise measurement procedures may include quantifying the noise levels, defining where and under what operating condition the noise <NUM> occurs, and defining its characteristics (such as dominant or signature frequency/frequencies using Fourier analysis, neural network training based on identifying certain noise patterns, and the like).

Next, the procedures may include analyzing the noise <NUM> based on noise characteristics such as signature frequencies. Acoustic signature-based analysis may use the frequency spectrum (e.g., in a range between <NUM> to <NUM>, produced by FFT) to distinguish different noise patterns. The signature frequencies of pre-determined noises (such as those from the computer simulations, and/or from controlled testing runs of power generation system <NUM>), or combination of the signature frequencies may be used for analyzing the noise <NUM>.

For example, the acoustic signature-based analysis may reveal that the noise <NUM> has a low frequency tone (about <NUM> or lower) prognoscative of the combustor <NUM> instability problem(s). Multichannel measurements (such as in-flow acoustic measurements using the microphones <NUM>, which may be split into different groups and configured to detect acoustic waves coming from different directions) may be used to determine the traveling direction of the noise <NUM>. The travel direction of the noise <NUM> may be the same as the exhaust flow direction, the opposite direction, or stationary (a standing wave). If the measurement shows the traveling direction of the noise <NUM> is the same as the exhaust flow direction, the combustor <NUM> may be the source. The other two possibilities (the opposite direction and stationary) may indicate that the exhaust <NUM> causes the problem.

In a second example, the noise spectrum (e.g., frequency contents) may indicate the noise <NUM> is a mid or high frequency tone (higher than <NUM>) related to aerodynamic phenomena of the exhaust <NUM>, such as vortex shedding or turbulent buffeting resulted from acoustic or structural vibration resonance. Identifying acoustic resonances may entail in-flow acoustic measurements using the microphones <NUM> at strategic locations in the exhaust <NUM>. Identifying possible vibration resonances may include using impulse response testing, calculations and/or computer simulations that may help to identify structural elements involved in the vibration resonance issue.

In a third example, the noise spectrum of the noise <NUM> may include a broadband noise profile caused by turbulence, which may be caused by the turbines (such as HP turbine engine <NUM> and LP turbine engine <NUM>), the exhaust <NUM>, or a combination thereof. Model-based simulation, such as thermodynamic models, finite element analysis models (FEA), Computational Fluid Dynamics (CFD) simulation, may help to identify the root cause of the turbulence (e.g., increased airflow rate).

In addition to responses to real time data recording/analysis, the CAMAD <NUM> may also continuously log the real time data into one or more databases <NUM>. Using the historical data from continuous recording, the CAMAD <NUM> may detect and recognize deviation from the typical noise profile and grow or otherwise add to the profile data for future reference. Continuously monitoring and/or logging of the collected data may enable operators to recognize the change for early failure detection based on the analysis of historical data over the life time of the monitored system/component (e.g., baseline signatures recorded during system initiation may be used to track the system/component over the time as the system/component signatures deviate from the initial baseline). Maintaining real-time, continuous monitoring of noise levels may aid the operators of the power generation system <NUM> in complying with different safety/environment requirements, such as Environment, health and safety (EHS), Occupational Safety and Health Act (OSHA), European Union (e.g., Germany Technical Instructions on Noise Abatement (TA LARM)), New Zealand, and Australia requirements.

The HMI <NUM> may be used to visually display the data output from the acoustic monitoring and analysis module <NUM> and intelligent diagnostic module <NUM>. By reviewing the displayed data from the HMI <NUM>, the operators may monitor the performance of the power generation system <NUM> and track potential problems indicated by the intelligent diagnostic module <NUM>. The operator may, via the HMI <NUM>, interact with the acoustic monitoring and analysis module <NUM> or intelligent diagnostic module <NUM> to conduct further monitoring (e.g., over a longer time period), to execute advanced data processing (e.g., special filtering), and/or to execute diagnostic routines based on historical events. The operator may also, via the HMI <NUM>, send instruction(s) to the local control system <NUM> to cause the power generation system <NUM> to preform intended operation(s) based on the instruction(s). In one or more embodiments, turbine control system <NUM> may include monitor(s) to facilitate the remote access of the CAMAD system <NUM>. Additionally, or alternatively, the CAMAD may be monitored through a virtual machine in a cloud.

During operations, the operators of the power generation system <NUM> may continuously monitor the noise performance from both near field and far field. For example, operators may want lower or quieter near field noise limits due to contaminated high far field noise, or the operators may want far field guarantees for certain noises and/or noise levels without environmental noise pollution. The CAMAD system <NUM> may provide a more effective way to enable verifying the far field noise by continuously monitoring the data to find out if the noise is actually related to the power generation system <NUM> or affected by other noises from the environment.

<FIG> illustrates schematically a deployment diagram of near field and far field microphone arrays that may be used by the CAMAD system <NUM>. In this illustrated embodiment, a near field (NF) microphone array <NUM> and a far field (FF) microphone array <NUM> may be utilized to detect the noise <NUM> generated from the power generation system <NUM> in a near field and far field respectively. The near field may be limited to a distance from the source of sound (such as the power generation system <NUM>), e.g., within <NUM> - <NUM> meters from the source of sound. The far field may begin at where the near field ends and extend to infinity (theoretically), e.g., between <NUM>-<NUM> meters, <NUM>-<NUM>, <NUM>-<NUM> meters, <NUM>-<NUM> meters or more.

As previously discussed, microphone positions of the NF microphone array <NUM> and the FF microphone array <NUM> may be determined by the operators of the power generation system <NUM> based on operational, safety, or environmental requirements. For example, to obtain more reliable noise measurements from certain components of the power generation system <NUM>, at least a portion of the NF microphone array <NUM> is positioned inside the enclosure (e.g., building) of the turbine system <NUM>. For another example, a residential area in the far field may be chosen as one of the positions of the FF microphone array <NUM> for such as when environment noise levels may be of concern.

Different and/or similar type of microphones may be used for the NF microphone array <NUM> and the FF microphone array <NUM>. The NF microphone array <NUM> may be communicatively connected to a NF multi-channel data acquisition module <NUM> that may control data acquisitions of each microphone in the NF microphone array <NUM>, data pre-processing (e.g., analog-to-digital signal conversion if signals sent from the NF microphone array <NUM> are not digital signals), and data communications between the NF multi-channel data acquisition module <NUM> and the CAMAD system <NUM>. Similarly, the FF microphone array <NUM> may be communicatively connected to a FF multi-channel data acquisition module <NUM> that controls data acquisitions of each microphone in the FF microphone array <NUM>, data pre-processing (e.g., analog-to-digital signal conversion if signals sent from the FF microphone array are not digital signals), and data communications between the FF multi-channel data acquisition module <NUM> and the CAMAD system <NUM>.

The power and connectivity supporting the microphones (including NF and FF microphone arrays <NUM> and <NUM>) and the data acquisition modules (including the NF and FF multi-channel data acquisition modules <NUM> and <NUM>) may be different depending on the locations and/or the surrounding environment. For example, the NF multi-channel data acquisition modules <NUM> may be connected to a data collection module <NUM> via connection cable(s) <NUM> (e.g., coax cables). While the FF multi-channel data acquisition modules <NUM> may be first connected to a Power over Ethernet (PoE) module <NUM> via powered Ethernet cable(s) <NUM>. The PoE module <NUM> may be further connected to the data collection module <NUM>. In one or more embodiments, the output signals from the multi-channel data acquisition module <NUM> may be transmitted to the data collection module <NUM> wirelessly (e.g., via a wireless network). In such embodiment(s), the data collection module <NUM> and the multi-channel data acquisition module <NUM> may be equipped with components related to wireless communication to support wireless data transmissions.

The data collection module <NUM> may collect output signals via the NF and FF multi-channel data acquisition modules <NUM> and <NUM>, transform the collected signals into the recorded acoustic signals <NUM>, and transmit the recorded acoustic signals <NUM> to the CAMAD system <NUM> for further processing and analysis. Based on the processed/analyzed data, the local control system <NUM> may generate the control signals <NUM> and send the control signals <NUM> to the power generation system <NUM> for performing certain tasks or control actions. After completing the intended tasks, new noise performance may be evaluated by the operator of the power generation system <NUM> through the CAMAD system <NUM>. The evaluations may compare real-time noise performance to the previous noise performance (i.e., before the completion of the intended tasks), which is part of the historical data from continuous recording. As described previously, the continuous recording may provide historical data for Root Cause Analysis (RCA) using system and component noise signatures at near field and far field.

The CAMAD system <NUM> may provide for synchronization mechanisms between the near field (NF) and the far field (FF). For example, the operators of power generation system <NUM> may desire lower NF noise ranges due to contaminated high far field noise. For example, the FF (e.g., <NUM> kilo-meter distance from power generation system <NUM>) noise level may excess a pre-determined limit. Analysis from the CAMAD system <NUM> may show the NF noise level is still within the limit. Further, the diagnose from CAMAD system <NUM> may indicate the high FF noise level is due to a background noise, and there may be no indication showing the power generation system <NUM> caused the issue of high FF noise level. Therefore, no further action may be needed for the power generation system <NUM>. Such observed events (e.g., high FF noise level above the limit) may be logged into one or more databases <NUM> for recording or further investigation.

In another example, a "rumble" noise detected by the FF microphone array <NUM> may be considered as a sign of a potential unexpected maintenance event for the power generation system <NUM>. Analysis from the CAMAD system <NUM> may show the FF rumble noise has a signature frequency of around <NUM>. However, the NF noise measurement logs recorded at the time when the FF rumble noise was detected may show no evidence of that a similar noise pattern has been captured by the NF microphone array <NUM>. Further investigation may reveal that the rumble noise was generated by a nearby vibrating source in the far field.

<FIG> illustrates a flow diagram of a process <NUM> suitable for processing acoustic signals communicated from the near field and far field microphone arrays <NUM> and <NUM> of <FIG>. The process <NUM> may be implemented as computer instructions or code executable by the processor(s) <NUM> and stored in the memory <NUM>. The NF microphone array <NUM> may measure or otherwise transmit signals representative of the noise <NUM> generated from the power generation system <NUM> (block <NUM>). The NF multi-channel data acquisition module <NUM> may be used to control the NF noise measurement process and send NF noise measurement <NUM> to the data collection module <NUM>. Similarly, the FF microphone array <NUM> may measure or otherwise transmit signals representative of the noise <NUM> generated from the power generation system <NUM> (block <NUM>). The FF multi-channel data acquisition module <NUM> may be used to control the FF noise measurement process and send FF noise measurement <NUM> to the data collection module <NUM>. The data collection module <NUM> may collect the NF noise measurement <NUM> (block <NUM>) and the FF noise measurement <NUM> (block <NUM>), respectively, and send collected measurements to the CAMAD system <NUM>.

The CAMAD system <NUM> may synchronize the NF noise measurement <NUM> and FF noise measurement <NUM> (block <NUM>) before performing processing, analysis, and further diagnosis. Data synchronization may be used to synchronize data between NF and FF noise measurements, and to update changes automatically between the NF and FF noise measurements, for example, to maintain data consistency within the CAMAD system <NUM>. Synchronized NF noise data <NUM> and synchronized FF noise data <NUM> may be logged into one or more databases <NUM>. The synchronization may tie the NF noise measurement <NUM> to the FF noise measurement <NUM> using data identifiers (e.g., tags) so that the following data processing and analysis may locate appropriate data blocks in one or more databases <NUM> using the data identifiers embedded into the synchronized NF noise data <NUM> and synchronized FF noise data <NUM>. For example, the data identifiers may include tags that include the times when the NF/FF noise measurements were recorded, locations for the recordings, ambient data for the recordings (e.g., pressure, temperature, humidity). The tags may be used by the CAMAD system <NUM> to locate, for a detected noise pattern shown in the FF noise measurement <NUM>, the corresponding NF noise measurement <NUM> that has matched time tags to the FF noise measurement <NUM>.

The CAMAD system <NUM> may perform the data synchronization with the data collection module <NUM>. Different synchronization options may be implemented depending on deployments of the NF and FF microphone arrays <NUM> and <NUM>. For example, the synchronization options may include using data processor time embedded in a local data processor (e.g., data processor in the data collection module <NUM>), GPS time, IEEE <NUM> protocol, other suitable synchronization protocols, or combinations thereof. As mentioned earlier, environmental parameters such as temperature, pressure, humidity, and so on, may also be logged with the time.

After the data synchronization, the CAMAD system <NUM> may process and analyze the synchronized NF noise data <NUM> and the synchronized FF noise data <NUM> (block <NUM>) to create NF noise signature <NUM> and the FF noise signature <NUM>. As described previously, the data processing may include using different filtering techniques to remove unwanted noise(s) in time and/or frequency domain, such as using random noise filters, finite impulse response filters, adaptive filters, band-pass filter, harmonic filters, other suitable signal processing techniques, or combinations thereof. Based on the processed data, noise signatures may be created using the Fourier-based analysis in the frequency domain. Noise signatures may also be created via deep learning, for example by training one or more neural networks on baseline data. Likewise, noise signatures may be created via other techniques such as data mining (e.g., creating baseline noise clusters as signatures), state vector machine training, via expert systems (e.g., a human expert providing rules, including fuzzy rules, that define a baseline), and so on.

During operations of the power production system <NUM>, the NF noise signature <NUM> and the FF noise signature <NUM>, combined with other data (such as data identifiers), may be used by the CAMAD system <NUM> to detect certain events (decision <NUM>) that may provide indications of potential issues that may lead to unwanted maintenance. For instance, the detected events may be related to vibrations and noises caused by a shifting of certain component of the power generation system <NUM>. The CAMAD system <NUM> may compare a detected noise pattern identified by the FF noise signature <NUM> in data analysis of the FF noise measurement <NUM>, to a similar noise pattern identified by the NF noise signature <NUM> in data analysis of the NF noise measurement <NUM> (block <NUM>), which has matched tags (e.g., time tags) to the FF noise measurement <NUM>. If the detected noise pattern identified by the FF noise signature <NUM> matches with the noise pattern identified by the NF noise signature <NUM>, the CAMAD <NUM> may perform Root Cause Analysis (RCA) (block <NUM>) using the FF noise signature <NUM>, the NF noise signature <NUM>, and other system and/or component noise signatures at near field and far field. The CAMAD <NUM> may also generate warning message or alert to notify operators of the power generation system <NUM> and display the result from the RCA (if available). The warning message, alert, and RCA result may be displayed via HMI <NUM>, and/or other suitable devices.

If, for the detected noise pattern shown in the FF noise measurement <NUM>, the FF noise signature <NUM> cannot match (decision <NUM>) with the NF noise signature <NUM>, the CAMAD <NUM> may log a detected event (e.g., noise pattern shown in the FF noise measurement <NUM>) into one or more databases <NUM> for future reference, and/or send continuous recording signals to the NF and FF microphone arrays <NUM> and <NUM> to continuously measure the noise <NUM> and update the database <NUM>. In one or more embodiments of the process <NUM>, the CAMAD <NUM> may be used for further analysis. The further analysis may use measurements from other type(s) of sensing device(s)/system(s) based on other physical aspects (e.g., non-acoustic changes) of the power generation system <NUM>. Different sensor types may be used in addition to the microphones, including pressure sensor, temperature/thermal sensor, vibration sensor, position sensor, optical sensor, and/or the like. The additional sensing devices may be incorporated to the monitoring system (e.g., the CAMAD system <NUM>) to provide additional information of the monitored power generation system <NUM>. For example, an abnormal noise pattern may be detected by the NF microphone array <NUM> surrounding the power generation system <NUM>. The CAMAD system <NUM> may further check vibration sensors deployed around the power generation system <NUM> to identify any abnormal vibration being detected. In addition, the CAMAD system <NUM> system may check optical sensors to verify certain alignments (e.g., alignments between stationary and rotating or components), which may indicate that certain component of the power generation system <NUM> have shifted, generating vibrations and noises detected by the vibration sensors, optical sensors, and microphones. In another example, a fuel pump used to operate the power generation system <NUM> may have a leakage. A pressure sensor may detect a pressure drop while the pump continues running and transmit the pressure drop to the CAMAD system <NUM>. The CAMAD system <NUM> may check the noise signatures from the NF noise signature <NUM> and/or the FF noise signature <NUM> for further verification to minimize or eliminate false alarm caused by pressure sensor malfunction.

A power system including a turbine engine (e.g., the power generation system <NUM> including a gas turbine engine) is used as an example embodiment in present disclosure, however, it should be understood that the techniques presented herein is not intended to be limited to the turbine generator systems/packages. The disclosed techniques may be used to other types of power generation systems or turbo machinery that have noise generating components (e.g., fans, pumps, compressors, motors, turboexpanders, and so on) that may be monitored by using acoustic sensors, other suitable sensing devices, or combinations thereof.

Claim 1:
A system, comprising:
an acoustic monitoring, analysis, and diagnostic system comprising a processor configured to:
receive NF noise signals from a near field (NF) microphone array, wherein the NF microphone array (<NUM>) measures noises generated from a power generation system (<NUM>) in a near field, which comprises a turbine system (<NUM>) inside an enclosure;
receive FF noise signals from a far field (FF) microphone array, wherein the FF microphone array (<NUM>) measures noises generated from the power generation system (<NUM>) in a far field;
derive NF noise measurements and FF noise measurements based on the signals;
synchronize the NF noise measurements and the FF noise measurements to create synchronized NF noise data and synchronized FF noise data;
analyze the synchronized NF noise data and synchronized FF noise data to create a NF noise signature and an FF noise signature;
diagnose one or more root causes of noises generated from the power generation system (<NUM>) based on the NF noise signature and FF noise signature; and
report the one or more root causes of the noises generated from the power generation system (<NUM>);
characterised in that:
at least a portion of the NF microphone array (<NUM>) is positioned inside the enclosure of the turbine system (<NUM>).