Optimization of cyber-physical systems

Methods and systems for optimizing performance of a cyber-physical system include training a machine learning model, according to sensor data from the cyber-physical system, to generate one or more parameters for controllable sensors in the cyber-physical system that optimize a performance indicator. New sensor data is collected from the cyber-physical system. One or more parameters for the controllable sensors are generated using the trained machine learning module and the new sensor data. The one or more parameters are applied to the controllable sensors to optimize the performance of the cyber-physical system.

BACKGROUND

Technical Field

The present invention relates to optimization of key performance indicators for a cyber-physical system and, more particularly, to continuous, local optimization of sensor settings within a cyber-physical system.

Description of the Related Art

Cyber-physical systems, such as power stations, include a number of mechanical, physical, or otherwise tangible systems that operate to generate some useful output. These cyber-physical systems are monitored by a variety of sensors that track physical conditions of the system. The gathered information can be used to optimize the cyber-physical system's output.

SUMMARY

A method for optimizing performance of a cyber-physical system includes training a machine learning model, according to sensor data from the cyber-physical system, to generate one or more parameters for controllable sensors in the cyber-physical system that optimize a performance indicator. New sensor data is collected from the cyber-physical system. One or more parameters for the controllable sensors are generated using the trained machine learning module and the new sensor data. The one or more parameters are applied to the controllable sensors to optimize the performance of the cyber-physical system.

A method for optimizing performance of a cyber-physical system includes training an artificial neural network, according to sensor data from the cyber-physical system, to generate one or more parameters for controllable sensors in the cyber-physical system that optimize a performance indicator. The artificial neural network includes a hidden layer that has radial basis function neurons that accept sensor data inputs from a plurality of sensors and that further has a constant value neuron. New sensor data is collected from the cyber-physical system. One or more parameters for the controllable sensors are generated using the trained artificial neural network and the new sensor data. The one or more parameters are applied to the controllable sensors to optimize the performance of the cyber-physical system.

A system for optimizing performance of a cyber-physical system includes a model trainer configured to train a machine learning model, according to sensor data from the cyber-physical system, to generate one or more parameters for controllable sensors in the cyber-physical system that optimize a performance indicator. An optimizer is configured to generate one or more parameters for the controllable sensors using the trained machine learning model and new sensor data. A sensor interface is configured to collect the new sensor data from the cyber-physical system and to apply the one or more parameters to the controllable sensors to optimize the performance of the cyber-physical system.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

In accordance with the present invention, systems and methods are provided monitor and optimize the performance of a cyber-physical system. The present embodiments make use of a continuously trained neural network model, based on a radial basis function, to generate parameters that optimize the cyber-physical system in view of the latest collected data. The present embodiments furthermore recognize a distinction between controllable sensors, where the measured value can be directly controlled by one or more parameters within the cyber-physical system, and non-controllable sensors. This makes it possible to optimize parameters for the controllable sensors, while still monitoring the values measured by the non-controllable sensors.

Referring now in detail to the figures in which like numerals represent the same or similar elements and initially toFIG.1, a cyber-physical system102is illustratively depicted in accordance with one embodiment of the present invention. The system102is monitored by a set of sensors104, which can each be either controllable or non-controllable. The sensors104record information about the cyber-physical system102and pass that information to a monitoring and optimization system106. The monitoring and optimization system106trains a machine learning model using collected data and optimizes parameters of the cyber-physical system102to optimize one or more performance indicators.

The cyber-physical system102can be any appropriate system that integrates a physical or mechanical process with distributed monitoring. It is particularly contemplated that the cyber-physical system102can be implemented as a power plant, but it should be understood that the present principles apply to any such system.

The physical status of the cyber-physical system102changes over time. Effects that causes changes to this status include environmental differences, including weather differences and seasonal differences, electricity loads, and the aging, deterioration, and breakdown of physical components. Thus, even if the performance of the cyber-physical system was optimized at one point with a particular set of parameters, those parameters will not produce optimal outputs forever.

The sensors104can monitor any physical or electronic process or system within the cyber-physical system. Exemplary sensors104include environmental sensors that measure ambient or environmental information, such as temperature sensors, humidity sensors, and air pressure sensors; operational sensors that measure characteristics of the physical process, such as accelerometers, counters, and scales; and electronic sensors that measure characteristics of electronic or software processes, such as logs kept by software programs within the cyber-physical system102. It should be understood that any given sensor block104can include multiple individual sensors, and that sensors104can be integrated with different physical machines or systems within the cyber-physical system102. Any number of sensors104can be employed, and it is particularly contemplated that many such sensors104can all be used to provide real-time information about many different aspects of the cyber-physical system102.

The sensors104are identified as being either controllable or non-controllable. A controllable sensor is one that measures a property of the cyber-physical system102that can be controlled by some parameter of the cyber-physical system102. For example, a sensor that measures the temperature of ambient air can be a controllable sensor in an embodiment where a heating/cooling system is present, because control of the heating/cooling system is available to control the temperature. In another example, however, a sensor that measures the temperature of ambient air would not be a controllable sensor if it were exposed to the outside air, because there would be no process by which the cyber-physical system102could control that property.

In some embodiments, the sensors104can be arranged as “Internet of Things” devices, with each having respective communications capabilities that interface with a communications network within the cyber-physical system102. For example, each sensor104can communicate with a wireless access point to send its information to the monitoring and optimization system106. In an alternative embodiment, the sensors104can communicate with the monitoring and optimization system106via a mesh network. It should be understood that any form of wired or wireless communications can be used instead of these specific examples.

The monitoring and optimization system106will be described in greater detail below. The system106has the ability to issue commands to one or more parts of the cyber-physical system102to change the parameters of its operation, responsive to changes in the condition of the cyber-physical system102and based on performance indicators reported by the cyber-physical system102, to maintain optimal performance. In some embodiments, the monitoring and optimization system106optimizes according to one or more performance indicators, either to maximize or minimize those indicators. Following the example of a power plant, the indicator may be a heat rate, which measures the efficiency of the power plant, lower heat rates indicating a higher efficiency. The monitoring and optimization system106learns a model of the relationships between measured values from the sensors104and the resulting effects on the performance indicator(s) and uses the learned model to set parameters for the controllable sensors104.

Referring now toFIG.2, an illustration of a machine learning model for the monitoring and optimization system106is shown. An exemplary artificial neural network (ANN)200is shown. An ANN is an information processing system that is inspired by biological nervous systems, such as the brain. The key element of ANNs is the structure of the information processing system, which includes a large number of highly interconnected processing elements (called “neurons”) working in parallel to solve specific problems. ANNs are furthermore trained in-use, with learning that involves adjustments to weights that exist between the neurons. An ANN is configured for a specific application, such as pattern recognition or data classification, through such a learning process.

ANNs demonstrate an ability to derive meaning from complicated or imprecise data and can be used to extract patterns and detect trends that are too complex to be detected by humans or other computer-based systems. The structure of a neural network is known generally to have input neurons (in this case, shown as the sensors104) that provide information to one or more “hidden” neurons (in this case, a set of radial basis function (RBF) blocks202). Connections204between the neurons are weighted. The weighted inputs from the sensors104are then processed by the RBF blocks202according to a function that will be described in greater detail below, with weighted connections204between the layers. Although it is specifically contemplated that only one RBF layer need be used in this embodiment, it should be understood that additional layers, performing different functions, can also be used. The hidden layer also includes a constant neuron206that provides a constant output value. Finally, an output neuron208(in this case, a combination of the weighted outputs of the hidden layer according to a function ƒ(x)) accepts and processes weighted input from the last set of hidden neurons.

This represents a “feed-forward” computation, where information propagates from input neurons to the output neuron. Upon completion of a feed-forward computation, the output is compared to a desired output available from training data. The error relative to the training data is then processed in “feed-back” computation, where the hidden neurons and input neurons receive information regarding the error propagating backward from the output neurons. Once the backward error propagation has been completed, weight updates are performed, with the weighted connections204being updated to account for the received error. This represents just one variety of ANN.

During feed-forward operation, the sensors104each provide an input signal in parallel to a respective row of weights204. The weights204each have a respective settable value, such that a weight output passes from the weight204to a respective RBF block202to represent the weighted input to the RBF block202. In software embodiments, the weights204may simply be represented as coefficient values that are multiplied against the relevant signals.

Referring now toFIG.3, a high-level view of a method for optimizing the cyber-physical system102is shown. Block302trains a machine learning model, for example an ANN200as described above. This training process is initially based on a set of training data, for example a set of historical sensor readings and associated performance indicators.

Block304collects updated sensor readings from the sensors104to determine the present state of the cyber-physical system102. Block306uses the up-to-date sensor readings as an input to the trained model to identify parameters that will optimize the cyber-physical system102. Block306sets the identified parameters in the cyber-physical system102accordingly.

Block308determines whether to retrain the model. It should be understood that this determination may alternatively be performed in parallel with the optimization of block306or may be on a separate cycle. As will be described in greater detail below, block308determines whether a predicted set of performance indicators matches a measured set of performance indicators. If the difference is greater than a threshold value, processing returns to block302to retrain the model with the latest sensor readings. If not, processing returns to block304to update the sensor readings and perform a new optimization.

Referring now toFIG.4, additional detail is shown on the training of the model in block302. Block402collects historical sensor data that has been collected by the sensors104. It should be noted that “historical sensor data” need not include only the data that existed at the time block302is first executed, but may also include sensor data that was collected since that time. All sensor data can be stored in a database at the monitoring and optimization system106or, in some embodiments, only a subset of the sensor data may be stored. Block404determines the number of RBF blocks202to use in the model and block406trains the model with the determined number of RBF blocks202using the historical sensor data. The number of RBF blocks202can be set as half the number of input sensors as a default. For example, if there are three thousand sensors104, including controllable and non-controllable sensors, the default number of RBF blocks202can be set to 1500 as a default.

Learning the model in block406uses the parameter m, which dictates how many RBF blocks202are in the model. Historical data is used to train the model using, for example, the following objective function:

minα⁢∑i=1m⁢f⁡(x)-αi⁢k⁡(ci,x)-b22+β⁢α22
where x represents a vector of sensor data from the sensors104, ƒ(x)=Σi=1mαik(ci, x)+b, a is a vector of weight coefficients between the RBF blocks202and the output neuron208, b is a weight coefficient between the constant neuron206and the output neuron208, β is a bias of the model ƒ(xnew) that is learned by the neural network, k(ci, x) is the function eλi∥ci−x∥22, λiis the control bandwidth of a Gaussian kernel and is a parameter that is learned by the neural network, and ciis a support vector that is learned by the neural network and that has the same dimension as x.

Once model training is complete, the learned model can be used to predict the performance indicators, using new sensor reading values as input. This is expressed as:

f⁡(xnew)=∑i=1m⁢αi⁢k⁡(ci,xnew)+b
where xnewis a vector of the new sensor values.

Referring now toFIG.5, additional detail on the optimization of the cyber-physical system102is shown. Block502calculates the gradient of the function ƒ(x), using the most recent sensor data, as:

Block504determines the optimized parameters, which relate to controllable sensors. The performance indicators can be either maximized or minimized, depending on how they relate to the performance of the cyber-physical system102. Following the example of using heat rate for a power station, a smaller heat rate indicates superior power station efficiency, so the optimization will reduce the performance indicator in this example to a degree that is controlled by a hyper-parameter η. This is expressed as:

xcontrollable=xcontrollable+η⁢⁢∂f∂xcontrollable
where xcontrollablerepresents the subset of the sensors x that are controllable. Block506then applies the determined optimized parameters to the controllable sensors in the cyber-physical system102.

Referring now toFIG.6, additional detail is shown regarding the determination of whether to retrain the model in block308. Block602uses the model's current parameters to determine an expected performance indicator or indicators. Block604determines the actual performance indicators, as reported by the cyber-physical system102. Block606determines whether a difference between the actual performance indicators and the predicted performance indicators exceeds a threshold. If so, this indicates that the state of the cyber-physical system102has changed and that the model needs to be retrained.

Referring now toFIG.7, additional detail on the system106for monitoring and optimizing a cyber-physical system102is shown. The system106includes a hardware processor702and a memory704and may optionally include one or more functional modules. In some embodiments, the functional modules can be implemented as software that is stored in the memory704and that is executed by the processor702. In other embodiments, one or more functional modules can be implemented as one or more discrete hardware components in the form of, e.g., application-specific integrated chips or field programmable gate arrays.

A sensor interface712collects information from the sensors104via any appropriate wired or wireless communications medium and protocol. Sensor information is stored in memory704in a sensor database706. The stored sensor information is used by model trainer708to train a machine learning model, such as ANN200and, when predicted performance indicators deviate from actual performance indicators, to retrain the machine learning model using up-to-date sensor data. An optimizer710uses the sensor data and the trained model200to determine a set of parameters for controllable sensors and applies the determined parameters to the controllable sensors via sensor interface712.