Cloud-Connected Smart Sensing for Resource Dispenser Monitoring
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Solution Overview
Problem
There is a need to effectively monitor and manage resource dispensers in facilities, such as restroom fixtures, to optimize maintenance schedules, reduce costs, and enhance user experience by preventing empty or malfunctioning dispensers, which existing technologies have not adequately addressed.
Innovation Solution
A cloud-connected smart sensing system using IoT architecture that communicates through end point devices and facility gateways to a cloud network, employing sensing subsystems to detect resource levels and transmit data for remote processing, enabling remote monitoring and management of resource dispensers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual monitoring of resource dispensers is performed, then operational simplicity is maintained, but resource management efficiency and user experience deteriorate due to empty or malfunctioning dispensers
Solution Approach 1:
The resource dispenser system performs self-monitoring through integrated sensors that automatically detect resource levels and operational status without requiring manual inspection. The system self-reporting capabilities enable automatic transmission of status data to the central platform, eliminating the need for manual monitoring while maintaining operational simplicity.
Solution Approach 2:
The system implements continuous feedback loops where sensors monitor resource levels and operational parameters, transmit this data to a central platform, and trigger automated alerts or notifications when thresholds are exceeded. This feedback mechanism enables proactive resource management and predictive maintenance, significantly improving resource management efficiency.
2Reliability
If frequent manual inspections are conducted, then resource availability is improved, but time consumption and operational costs increase
Solution Approach 1:
The system performs preliminary monitoring actions by continuously tracking resource levels and operational status before actual resource depletion or failure occurs. Predictive analytics analyze trends in the data to forecast when resources will be depleted or when maintenance is needed, enabling proactive intervention that ensures resource availability without requiring frequent manual inspections.
Solution Approach 2:
Continuous feedback from sensors and automated alerts notify facility managers of resource status in real-time, eliminating the need for scheduled manual inspections. The system only requires attention when actual intervention is needed, dramatically reducing time loss while maintaining high resource availability through proactive management.
3Loss of information
If traditional monitoring methods are used, then system simplicity is maintained, but data collection capability and analytical insights deteriorate
Solution Approach 1:
The IoT platform serves multiple functions simultaneously: it collects data from various sensor types, stores information in a centralized database, performs predictive analytics, generates alerts, and provides a user interface for facility managers. This multi-functional platform consolidates what would otherwise require separate systems, making the enhanced data collection capability manageable despite the increased functionality.
Solution Approach 2:
The centralized IoT platform acts as an intermediary between the physical sensors in resource dispensers and the analytical processing needed to extract insights. It standardizes data collection from heterogeneous sensor types, normalizes the data format, and provides analytical processing capabilities, thereby enabling comprehensive data collection without requiring complex integration logic at each dispenser unit.
4Reliability
If reactive maintenance is performed, then maintenance costs are reduced, but facility reliability and user experience worsen due to unexpected failures
Solution Approach 1:
The system performs preliminary analysis of sensor data to predict potential failures before they occur. By analyzing trends in resource consumption patterns, operational parameters, and sensor readings, the system identifies early signs of degradation or malfunction, enabling scheduled maintenance during off-peak hours or before critical failures occur, thereby maintaining high facility uptime.
Solution Approach 2:
Continuous feedback from operational sensors and performance monitoring enables the system to detect anomalies and predict maintenance needs in real-time. This feedback-driven predictive maintenance approach optimizes maintenance scheduling to perform interventions only when necessary, improving facility reliability while maintaining cost efficiency by avoiding both premature and delayed maintenance.
Data Source
AI summary
Methods and systems for providing a resource dispenser status for a resource dispenser associated with a facility. One system includes an electronic processor. The electronic processor is configured to receive a set of signals. The electronic processor is also configured to compare a first signal included in the set of signals to a set of thresholds. The electronic processor is also configured to determine a resource dispenser status based on the comparison of the first signal to the set of thresholds. The electronic processor is also configured to generate and transmit the resource dispenser status to a remote device, wherein the remote device provides the resource dispenser status to a user.