Classifications

• • G—PHYSICS
  • G08—SIGNALLING
  • G08B—SIGNALLING SYSTEMS, e.g. PERSONAL CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
  • G08B29/00—Checking or monitoring of signalling or alarm systems; Prevention or correction of operating errors, e.g. preventing unauthorised operation
  • G08B29/02—Monitoring continuously signalling or alarm systems
  • G08B29/04—Monitoring of the detection circuits
  • G08B29/043—Monitoring of the detection circuits of fire detection circuits

Definitions

• the present disclosure belongs to the technical field of smoke detectors, and more particularly to smoke detector for identification of smoke detectors obstructed of light in an ambient.
• a key aspect of fire protection is to identify a developing fire emergency in a timely manner, and to alert the building's occupants and fire emergency organizations. This is the role of fire detection and alarm systems. These systems also self-monitor, identifying where within the building(s) alarms originate from and detecting when errors occur in wiring and connections that may hinder the system from working correctly.
• Document DE102012201703 providing an automatic fire detector for detecting fires, having a housing, wherein the housing comprising a measuring chamber for detecting smoke particles, having a sensor system for detecting a measured variable for evaluating the operational capability of the automatic fire detector, having an evaluation device for evaluating the operational capability of the automatic fire detector on the basis of the measured variable, the sensor system comprising at least one flow sensor for detecting a flow as the measured variable for evaluating the operational capability of the automatic fire detector.
• Document EP2270762B1 discloses an invention related to a smoke detector with a housing which has smoke passage openings and comprises a smoke detector and an alarm signalling device. The invention further relates to a method for checking the contamination of smoke passage openings of a smoke detector with a housing.
• the present disclosure discloses a smoke detector arranged to identify when obstructed in an ambient, comprising: a dark chamber with one or more inlet openings for receiving smoke of the ambient; a light-sensor arranged within the dark chamber; and an electronic data processor arranged to: capture an electrical signal output of the light-sensor for sampling the light o over a period of time; process the captured light sample signal with a pretrained machine-learning model to determine whether the light sample signal matches one or more predetermined light patterns of an obstructed smoke detector; determine a warning if the captured light sample is determined to be at least in part comprising a predetermined light pattern of an obstructed smoke detector.
• the fire alarm panel is connected to the system's initiating devices through either 2- or 4-wire circuits. This circuitry allows the control panel to monitor the state of its initiating devices, usually by zones, identifying whether the devices are in normal or alarm mode.
• a smoke detector arranged to identify when obstructed in an ambient, comprising: a dark chamber with one or more inlet openings for receiving the smoke of the ambient; a light-sensor arranged within the dark chamber; and an electronic data processor arranged to: capture an electrical signal output of the lightsensor for sampling the light of the over a period of time; process the captured light sample signal with a pretrained machine-learning model to determine whether the light sample signal matches one or more predetermined light patterns of an obstructed smoke detector; determine a warning if the captured light sample signal is determined to be at least in part comprising a predetermined light pattern of an obstructed smoke detector.
• the electronic data processor comprises a performance core configured to carry out light processing tasks and a low-power core configured to carry out smoke detection tasks.
• the electronic data processor is further arranged to input the captured light sample signal to the pretrained machine-learning model by aggregating the captured electrical signal output for a subperiod of time comprised within said period of time.
• the period of time is twenty-four hours.
• the electronic data processor is further arranged to feed the captured light sample signal to the pretrained machine-learning model by inputting the aggregated captured electrical signal output to an embedding neural network for increasing dimensionality.
• the light-sensor is a photoresistor, photodiode and/or phototransistor and/or wherein the light-sensor is a light sensor of a light-scattering detection arrangement.
• the pretrained machine-learning model is an artificial neural network.
• the pretrained machine-learning model is an artificial recurrent neural network, a convolutional neural network, and/or a long short-term memory.
• a computer-based method for providing a warning for identification of a smoke detector obstructed in an ambient said smoke detector comprising a dark chamber with one or more inlet openings for receiving the light of the ambient, a light-sensor arranged within the dark chamber and an electronic data processor, said method comprising the steps of: capturing by the electronic data processor an electrical signal output of the light-sensor for sampling the light over a period of time; processing by the electronic data processor the captured light sample signal with a pretrained machine-learning model to determine whether the light sample signal matches one or more predetermined light patterns of an obstructed smoke detector; determining by the electronic data processor a warning if the captured light sample signal is determined to be at least in part comprising a predetermined light pattern of an obstructed smoke detector.
• the light patterns are periodical daily patterns over said period of time.
• said method further comprising inputting the captured light sample signal to the pretrained machine-learning model by aggregating the captured electrical signal output for a subperiod of time comprised within said period of time, in particular aggregating by averaging the captured electrical signal output.
• the method comprises the sub-period of time of an hour.
• the method comprises feeding the captured light sample signal to the pretrained machine-learning model by inputting the aggregated captured electrical signal output to an embedding neural network for increasing dimensionality.
• the method comprises the pretrained machine-learning model is an artificial neural network, in particular the pretrained machine-learning model being an artificial recurrent neural network, a convolutional neural network, and/or a long short-term memory.
• Figure 3 Schematic representation of an embodiment approach of Al models' accuracy for identification of obstructed detectors.
• Figure 4 Schematic representation of an embodiment of a model pipeline.
• Figure 5 Schematic representation of an embodiment approach of values forthe 24 features of the optl for an obstructed and an unobstructed detector.
• Figure 6 Schematic representation of an embodiment of LSTM model architecture.
• Figure 7 Schematic representation of an embodiment of CNN model architecture.
• the present disclosure relates to a smoke detector for identification of smoke detectors obstructed of light in an ambient spot.
• a smoke alarm should be installed and maintained according to the manufacturer's instructions. When installing a smoke alarm, many factors influence where you will place the alarm, including how many are to be installed. Consider placing alarms along the escape path to assist in egress in limited-visibility conditions. In general, alarms should be placed in the centre of a ceiling or, if placed on a wall, should be installed not more than 12 inches away from the ceiling. If ceiling is pitched, the alarm should be installed within 36 inches of the peak but not within the apex of the peak (4 inches down from the peak) as shown in Figure 1.
• Optical smoke detectors use the scattered-light method.
• a LED transmits light to the measuring chamber, where it is absorbed by the labyrinth structure.
• smoke enters the measuring chamber, and the smoke particles scatter the light from the LED.
• the amount of light hitting the photo diode is converted into a proportional electrical signal.
• the first step to create the desired solution was to install a fire alarm test system, in a controlled environment.
• the smoke detectors were installed following all the installation rules' requirements, but to have valid data to train Al models and create a robust solution, half of the installed sensors were obstructed for 2 months. After that period some of those covers were removed and used in other sensors for the same timeperiod.
• Al models were trained in a training set to recognize certain types of patterns. They use various types of algorithms to reason over and learn from data, with the overarching goal of providing a reliable classification algorithm capable to distinguish between obstructed and unobstructed detectors.
• Random Forest RF
• Support Vector Machines SVM
• Convolutional Neural Networks CNN
• Long Short-Term Memory LSTM
• RF are an ensemble learning method (use multiple learning algorithms to obtain better predictive performance) for classification, regression and tasks that operates by constructing multiple decision trees at training time.
• classification tasks the output of the random forest is the class selected by most trees. For example, each tree in the classifications takes input from samples, features are then randomly selected, and are used in growing the tree at each node. In addition, every tree in the forest should not be pruned until the end of the task when the prediction is reached with a decision.
• this model reduces overfitting problem in decision trees and the variance which improves the accuracy, is usually robust to outliers and can handle them automatically. It is also a very stable algorithm - if a new data point is presented, the overall algorithm is not affected, it can impact one tree, but it is very hard to influence all the trees.
• SVM are supervised learning models with learning algorithms that analyse data for classification and regression analysis.
• a supportvector machine creates a hyperplane or set of hyperplanes in a high- or infinitedimensional space, which can be used for classification, regression, or tasks such as outlier's detection.
• a separation is achieved by the hyperplane that has the largest distance to the nearest training data point of a class.
• the SVM provides possible kernels, thus, we can choose a function which is not necessarily linear and can have different forms in terms of different data.
• SVM can be used for data that is not regularly distributed and have unknown distribution, generally avoid overfitting and performs well when there is a clear separation between classes. SVM can handle high dimensional data.
• a CNN is a class of artificial neural network (ANN).
• CNN are regularized versions of multilayer perceptron and multilayer perceptron, typically are fully connected networks, where each neuron in one layer is connected to all neurons in the next layer.
• the convolution and pooling layers perform feature extraction, whereas the fully connected layer, maps the extracted features into final output, as for example classification.
• the architecture consists of repetitions of a stack of several convolution layers and a pooling layer. Training a CNN network is a way of finding kernels in convolution layers and weights in fully connected layers that minimize differences between output predictions and the ground truth labels on a training set.
• a model performance through kernels and weights is considered using a loss function through forward propagation on a training dataset.
• the learnable parameters, kernels and weights, are updated according to the loss value through an optimization algorithm called backpropagation and gradient descent, where the goal is to minimize the loss.
• CNN have accomplished great achievements across a variety of domains in particular, the CNN model allows the use of a global average pooling.
• the advantages of applying global average pooling are: (1) the capacity to reduce the number of learnable parameters and (2) enables the CNN to accept inputs of variable size.
• LSTM is an artificial recurrent neural network (RNN) architecture used in deep learning.
• RNN recurrent neural network
• LSTM has feedback connections.
• neural networks we have a stack of layers containing nodes, where the input data (features) feed the nodes of the input layers and the information is combined as weights and passed to the next layer, until arrives the output layer.
• the expected output (target) is compared with the model's output and the weights are updated.
• the goal is to minimize loss (e.g. in terms of error).
• the gradient is calculated, which is, loss with concerning a particular set of weights, the weights are adjusted, and the process is repeated until the loss is minimum for an optimal set of weights. This process is called backtracking.
• LSTM networks are well-suited to perform predictions based on time series data, since there can be lags of unknown length between events in a time series. LSTM also handles noise and continuous values.
• the data forthe obstructed detectors model was recorded in the Bosch test system. It is collected data from 15 devices, of which 7 are covered and 8 are uncovered. The state (covered or uncovered) of each detector changes in time. This means that the cover in the devices is put on or taken off. The date in which these modifications are done is recorded to later label the recorded data.
• the metric recorded is the optical (optl).
• the sensors register the data within intervals of 15 to 90 minutes.
• the following pertains to data pre-processing. Like mentioned above, the data is received in intervals of about 15 to 90 minutes. In order to make the frequency between timestamps equal it was done an aggregation of the values to hourly intervals using the mean to calculate the value of the metric.
• LSTM Long short-term memory
• RNN recurrent neural network
• the LSTM model developed is composed of an embedding layer, a LSTM layer and a dense layer to give the output of the model.
• the embedding layer allows to convert the features into a vector representation of all the features.
• the model is then trained based on this vector.
• the parameters of each of the layers are computed using a hyperparameter optimization framework.
• the parameters that are optimized are the activation function and number of neurons of the LSTM layer and the batch size and number of epochs of the model.
• the optimal parameters depend on the data that is being train to the model. To give an example, for the 80/20 train-test split the optimal parameters are:
• CNN Convolutional neural network
• ANN artificial neural network
• the CNN has three main types of layers: convolutional layer, pooling layer and fully-connected layer.
• a model can have several convolutional and pooling layers but only one fully-connected layer (last layer).
• Each convolutional layer processes increasingly more complex information (starts by processing easier features).
• the pooling layers allow to reduce the dimensional complexity of the model while keeping the most significant information.
• the fully- connected layer maps the extracted features into a final output.
• the CNN starts by the embedding step.
• the data is then fed to three pairs of convolutional and pooling layers.
• a dense layer outputs the prediction of the model.
• the max pooling layer summarizes the presence of features in the input sequence. Pooling layers provide an approach to down sampling feature maps. The output after a max-pooling layer would be the feature map containing the most prominent features of the previous feature map.

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• Computer Security & Cryptography (AREA)
• Physics & Mathematics (AREA)
• General Physics & Mathematics (AREA)
• Fire-Detection Mechanisms (AREA)

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• 2022
  • 2022-10-14 WO PCT/IB2022/059867 patent/WO2024079510A1/en not_active Ceased
  • 2022-10-14 EP EP22803065.6A patent/EP4602584A1/de active Pending

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