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  • Leng, C.H.  (3)
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  • 1
    Online Resource
    Online Resource
    Real-time Machine Health Monitoring System using Machine Learning with IoT Technology
    EDP Sciences ; 2021
    In:  MATEC Web of Conferences Vol. 335 ( 2021), p. 02005-
    Details
    In: MATEC Web of Conferences, EDP Sciences, Vol. 335 ( 2021), p. 02005-
    Abstract: Machine health monitoring is the main focal point for now as many industries are evolving to industry 4.0. Industry 4.0 is the revolution in industrial that involve the Internet of Things (IoT) and artificial intelligence toward automation and data sharing for production efficiency improvement. The existing established methods for machine health monitoring were not in real-time and there was no real-time correction of data from the load and processing of data on the computer. In tracking machine health efficiency this approach wasn’t very successful. Real-time machine health monitoring can improve overall equipment effectiveness (OEE), reduce electricity consumption, minimize unplanned downtime, and extend machine lifetime. In this research paper, we propose to design a real-time machine health monitoring system using machine learning with IoT technology that can analyze the supply balancing condition on a 3-phase system. This system is built with compact physical hardware and can capture the electrical data from the load then send it to the server. The server will progress data and train the data using machine learning. The system was installed on a blender machine in a factory. In this research, a system which is able to monitor the machine operation and classify the operation stages of the machine was developed. Besides that, the system also capable to monitor the load balancing condition of the machine.
    Type of Medium: Online Resource
    ISSN: 2261-236X
    URL: Article
    DOI: 10.1051/matecconf/202133502005
    Language: English
    Publisher: EDP Sciences
    Publication Date: 2021
    detail.hit.zdb_id: 2673602-0
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Industrial Electrical Energy Consumption Forecasting by using Temporal Convolutional Neural Networks
    EDP Sciences ; 2021
    In:  MATEC Web of Conferences Vol. 335 ( 2021), p. 02003-
    Details
    In: MATEC Web of Conferences, EDP Sciences, Vol. 335 ( 2021), p. 02003-
    Abstract: In conjunction with the 4th Industrial Revolution, many industries are implementing systems to collect data on energy consumption to be able to make informed decision on scheduling processes and manufacturing in factories. Companies can now use this historical data to forecast the expected energy consumption for cost management. This research proposes the use of a Temporal Convolutional Neural Network (TCN) with dilated causal convolutional layers to perform forecasting instead of conventional Long-Short Term Memory (LSTM) or Recurrent Neural Networks (RNN) as TCN exhibit lower memory and computational requirements. This approach is also chosen due to traditional regressive methods such as Autoregressive Integrated Moving Average (ARIMA) fails to capture non-linear patterns and features for multi-step time series data. In this research paper, the electrical energy consumption of a factory will be forecasted by implementing a TCN to extract the features and to capture the complex patterns in time series data such daily electrical energy consumption with a limited dataset. The neural network will be built using Keras and TensorFlow libraries in Python. The energy consumption data as training data will be provided by GoAutomate Sdn Bhd. Then, the historical data of economic factors and indexes such as the KLCI will be included alongside the consumption data for neural network training to determine the effects of the economy on industrial energy consumption. The forecasted results with and without the economic data will then be compared and evaluated using Weighted Average Percentage Error (WAPE) and Mean Absolute Percentage Error (MAPE) metrics. The parameters for the neural network will then be evaluated and fined tuned accordingly based on the accuracy and error metrics. This research is able create a CNN to forecast electrical energy consumption with WAPE = 0.083 & MAPE = 0.092, of a factory one (1) week ahead with a small scale dataset with only 427 data points, and has determined that the effects of economic index such as the Bursa Malaysia has no meaningful impact on industrial energy consumption that can be then applied to the forecasting of energy consumption of the factory.
    Type of Medium: Online Resource
    ISSN: 2261-236X
    URL: Article
    DOI: 10.1051/matecconf/202133502003
    Language: English
    Publisher: EDP Sciences
    Publication Date: 2021
    detail.hit.zdb_id: 2673602-0
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    Online Resource
  • 3
    Online Resource
    Online Resource
    Design and Implementation of an Agricultural UAV With Optimized Spraying Mechanism
    EDP Sciences ; 2021
    In:  MATEC Web of Conferences Vol. 335 ( 2021), p. 02002-
    Details
    In: MATEC Web of Conferences, EDP Sciences, Vol. 335 ( 2021), p. 02002-
    Abstract: Agricultural UAVs are growing and developing rapidly throughout the whole world due to its extensive advantages. Agricultural drones are transforming on the way farming is being carried out. They are very suitable and agile for working in a large area of land and rough terrains with high efficiency. Agriculture drones are much bigger in size where they will have a larger and wider spraying span. They can increase and improve the efficiency of spraying more area of land in a shorter duration compared to a knapsack sprayer. The entire research design is based on quantitative research, which was conducted through simulation using SolidWorks, MATLAB and Ansys Fluent. SolidWorks was used for planning, modelling and visual ideation of the agricultural drone. Each of the individual components of the agricultural drone was measured, sketched and designed. MATLAB was used to simulate the agricultural drone to fly in a specific pattern to water a certain area. Velocity, acceleration, position and flight pathway graphs were plotted. These data were collected to observe how evenly the entire area being sprayed is fully covered. Ansys Fluent was used to display the velocity that the fluid will be flowing inside the nozzle and spraying it out.
    Type of Medium: Online Resource
    ISSN: 2261-236X
    URL: Article
    DOI: 10.1051/matecconf/202133502002
    Language: English
    Publisher: EDP Sciences
    Publication Date: 2021
    detail.hit.zdb_id: 2673602-0
    Location Call Number Limitation Availability
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    Online Resource
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