Internet of Things systems generate a large amount of sensor data that needs to be analyzed for extracting useful insights on the health status of the machine under consideration.Sensor data of all possible states of ...Internet of Things systems generate a large amount of sensor data that needs to be analyzed for extracting useful insights on the health status of the machine under consideration.Sensor data of all possible states of a system are used for building machine learning models.These models are further used to predict the possible downtime for proactive action on the system condition.Aircraft engine data from run to failure is used in the current study.The run to failure data includes states like new installation,stable operation,first reported issue,erroneous operation,and final failure.In the present work,the non-linear multivariate sensor data is used to understand the health status and anomalous behavior.The methodology is based on different sampling sizes to obtain optimum results with great accuracy.The time series of each sensor is converted to a 2D image with a specific time window.Converted Images would represent the health of a system in higher-dimensional space.The created images were fed to Convolutional Neural Network,which includes both time variation and space variation of each sensed parameter.Using these created images,a model for estimating the remaining life of the aircraft is developed.Further,the proposed net is also used for predicting the number of engines that would fail in the given time window.The current methodology is useful in avoiding the health index generation for predicting the remaining useful life of the industrial components.Better accuracy in the classification of components is achieved using the TimeImagenet-based approach.展开更多
Increased smart devices in various industries are creating numerous sensors in each of the equipment prompting the need for methods and models for sensor data.Current research proposes a systematic approach to analyze...Increased smart devices in various industries are creating numerous sensors in each of the equipment prompting the need for methods and models for sensor data.Current research proposes a systematic approach to analyze the data generated from sensors attached to industrial equipment.The methodology involves data cleaning,preprocessing,basics statistics,outlier,and anomaly detection.Present study presents the prediction of RUL by using various Machine Learning models like Regression,Polynomial Regression,Random Forest,Decision Tree,XG Boost.Hyper Parameter Optimization is performed to find the optimal parameters for each variable.In each of the model for RUL prediction RMSE,MAE are compared.Outcome of the RUL prediction should be useful for decision maker to drive the business decision;hence Binary cclassification is performed,and business case analysis is performed.Business case analysis includes the cost of maintenance and cost of non-maintaining a particular asset.Current research is aimed at integrating the machine intelligence and business intelligence so that the industrial operations optimized both in resource and profit.展开更多
文摘Internet of Things systems generate a large amount of sensor data that needs to be analyzed for extracting useful insights on the health status of the machine under consideration.Sensor data of all possible states of a system are used for building machine learning models.These models are further used to predict the possible downtime for proactive action on the system condition.Aircraft engine data from run to failure is used in the current study.The run to failure data includes states like new installation,stable operation,first reported issue,erroneous operation,and final failure.In the present work,the non-linear multivariate sensor data is used to understand the health status and anomalous behavior.The methodology is based on different sampling sizes to obtain optimum results with great accuracy.The time series of each sensor is converted to a 2D image with a specific time window.Converted Images would represent the health of a system in higher-dimensional space.The created images were fed to Convolutional Neural Network,which includes both time variation and space variation of each sensed parameter.Using these created images,a model for estimating the remaining life of the aircraft is developed.Further,the proposed net is also used for predicting the number of engines that would fail in the given time window.The current methodology is useful in avoiding the health index generation for predicting the remaining useful life of the industrial components.Better accuracy in the classification of components is achieved using the TimeImagenet-based approach.
文摘Increased smart devices in various industries are creating numerous sensors in each of the equipment prompting the need for methods and models for sensor data.Current research proposes a systematic approach to analyze the data generated from sensors attached to industrial equipment.The methodology involves data cleaning,preprocessing,basics statistics,outlier,and anomaly detection.Present study presents the prediction of RUL by using various Machine Learning models like Regression,Polynomial Regression,Random Forest,Decision Tree,XG Boost.Hyper Parameter Optimization is performed to find the optimal parameters for each variable.In each of the model for RUL prediction RMSE,MAE are compared.Outcome of the RUL prediction should be useful for decision maker to drive the business decision;hence Binary cclassification is performed,and business case analysis is performed.Business case analysis includes the cost of maintenance and cost of non-maintaining a particular asset.Current research is aimed at integrating the machine intelligence and business intelligence so that the industrial operations optimized both in resource and profit.
