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1. Deep-Learning
Maintenance Management of IoT
Empowered Smart Toilet
Abstract
The recent proliferation of Internet of Things (IoT) sensors has driven a myriad
of industrial and urban applications. Through analyzing massive data collected
by these sensors, the proactive maintenance management can be achieved
such that the maintenance
optimized. Despite recent progress in proactive maintenance management in
industrial scenarios, there are few studies on proactive maintenance
management in urban informatics. In this article, we present an integ
framework of IoT and cloud computing platform for the proactive maintenance
management in smart city. Our framework consists of: 1) an IoT monitoring
system for collecting time-
the equipment and 2) a hybrid deep learning model, namely, convolutional
bidirectional long short-term memory (CBLM) model for forecasting the
operating and ambient conditions based on the collected time
Learning-Driven Proactive
Maintenance Management of IoT-
Empowered Smart Toilet
The recent proliferation of Internet of Things (IoT) sensors has driven a myriad
of industrial and urban applications. Through analyzing massive data collected
by these sensors, the proactive maintenance management can be achieved
such that the maintenance schedule of the installed equipment can be
optimized. Despite recent progress in proactive maintenance management in
industrial scenarios, there are few studies on proactive maintenance
management in urban informatics. In this article, we present an integ
framework of IoT and cloud computing platform for the proactive maintenance
management in smart city. Our framework consists of: 1) an IoT monitoring
-series data of operating and ambient conditions of
hybrid deep learning model, namely, convolutional
term memory (CBLM) model for forecasting the
operating and ambient conditions based on the collected time-series data. In
The recent proliferation of Internet of Things (IoT) sensors has driven a myriad
of industrial and urban applications. Through analyzing massive data collected
by these sensors, the proactive maintenance management can be achieved
schedule of the installed equipment can be
optimized. Despite recent progress in proactive maintenance management in
industrial scenarios, there are few studies on proactive maintenance
management in urban informatics. In this article, we present an integrated
framework of IoT and cloud computing platform for the proactive maintenance
management in smart city. Our framework consists of: 1) an IoT monitoring
series data of operating and ambient conditions of
hybrid deep learning model, namely, convolutional
term memory (CBLM) model for forecasting the
series data. In

2. addition, we also develop a naïve Bayes classifier to detect abnormal
operating and ambient conditions and assist management personnel in
scheduling maintenance tasks. To evaluate our framework, we deployed the
IoT system in a Hong Kong public toilet, which is the first application of
proactive maintenance management for a public hygiene and sanitary facility
to the best of our knowledge. We collected the sensed data more than 33
days (808 h) in this real system. Extensive experiments on the collected data
demonstrated that our proposed CBLM outperformed six traditional machine
learning algorithms.