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Data reconciliation of indoor positioning data: Improve position data accuracy in warehouse environment
Data reconciliation of indoor positioning data: Improve position data accuracy in warehouse environment
This article focuses on improving indoor positioning data through data reconciliation. Indoor positioning systems are increasingly used for resource tracking to monitor manufacturing and warehouse processes. However, measurement errors due to noise can negatively impact system performance. Redundant measurement involves the use of multiple sensor tags that provide position data on the same resource, to identify errors in the physical environment. If we have measurement data from the entire physical environment, a map-based average measurement error can be determined by specifying the points in the examined area where measurement data should be compensated and to what extent. This compensation is achieved through data reconciliation, which improves real-time position data by considering the measurement error in the actual position as an element of the variance-covariance matrix. A case study in a warehouse environment is presented to demonstrate how discrepancies in position data from two sensor tags on forklifts can be used to identify layout-based errors. The algorithm is generally capable of handling the multi-sensor problem in the case of indoor positioning systems. The key points are as follows:
The layout-based error detection is determined with the indoor positioning system measurement error.
• This article shows how redundant measurements and data reconciliation can improve the accuracy of such systems.
• Improving the accuracy of position data with the layout-based error map using a data reconciliation algorithm.
Post Date: 2 July 2024
Indoor Positioning-based Occupational Exposures Mapping and Operator Well-being Assessment in Manufacturing Environment
Indoor Positioning-based Occupational Exposures Mapping and Operator Well-being Assessment in Manufacturing Environment
This research was motivated by the need for detailed information about the spatial and contextualized distribution of occupational exposures, which can be used to improve the layout of the workspace. To achieve this goal, the study emphasizes the need for position-related information and contextualized data. To address these concerns, the study proposes the use of Indoor Positioning System (IPS) sensors that can be further developed to establish a set of metrics for measuring and evaluating occupational exposures. The proposed IPS-based sensor fusion framework, which combines various environmental parameters with position data, can provide valuable insights into the operator’s working environment. For this, we propose an indoor position-based comfort level indicator. By identifying areas of improvement, interventions can be implemented to enhance operator performance and overall health. The measurement unit installed on a manual material handling device in a real production environment and collected data using temperature, noise, and humidity sensors. The results demonstrated the applicability of the proposed comfort level indicator in a wire harness manufacturing setting, providing location-based information to enhance operator well-being. Overall, the proposed framework can be used as a tool to monitor the industrial environment, especially the well-being of shop floor operators.
Post date 12 October 2023
Processing indoor positioning data by goal-oriented supervised fuzzy clustering for tool management
Processing indoor positioning data by goal-oriented supervised fuzzy clustering for tool management
Indoor positioning systems allow real-time tracking of tool locations. Tool utilization can be calculated based on positional data of the storage and manufacturing areas. Due to the uncertainty of the position measurements, estimation of the state of the tools is problematic when the distance urvival Indoor positioning systems allow real-time tracking of tool locations. Tool utilization can be calculated based on positional data of the storage and manufacturing areas. Due to the uncertainty of the position measurements, estimation of the state of the tools is problematic when the distance between the examined zones is less than the estimation error. We propose a goal-oriented supervised fuzzy clustering algorithm that utilizes the activity state of the tool, as the algorithm simultaneously maximizes the spatial distribution probability and the probability of a specific activity state occurring in a cluster. By weighting data points according to the time spent in the related states and positions, the resulting cluster weights can be interpreted as tool utilizations. The applicability of the developed method is presented through the processing of position data from crimping tools used by a wire harness manufacturer.
Indoor Positioning Systems Can Revolutionise Digital Lean
Indoor Positioning Systems Can Revolutionise Digital Lean
The powerful combination of lean principles and digital technologies accelerates wasteidentification and mitigation faster than traditional lean methods. The new digital lean (also referredto as Lean 4.0) solutions incorporate sensors and digital equipment, yielding innovative solutionsthat extend the reach of traditional lean tools. The tracking of flexible and configurable productionsystems is not as straightforward as in a simple conveyor. This paper examines how the informationprovided by indoor positioning systems (IPS) can be utilised in the digital transformation of flexiblemanufacturing. The proposed IPS-based method enriches the information sources of value streammapping and transforms positional data into key-performance indicators used in Lean Manufacturing.The challenges of flexible and reconfigurable manufacturing require a dynamic value stream mapping.To handle this problem, a process mining-based solution has been proposed. A case study isprovided to show how the proposed method can be employed for monitoring and improvingmanufacturing efficiency.
Real-Time Locating System in Production Management
Real-Time Locating System in Production Management
Real-time monitoring and optimization of production and logistics processes significantlyimprove the efficiency of production systems. Advanced production management solutions requirereal-time information about the status of products, production, and resources. As real-time locatingsystems (also referred to as indoor positioning systems) can enrich the available information, thesesystems started to gain attention in industrial environments in recent years. This paper providesa review of the possible technologies and applications related to production control and logistics,quality management, safety, and efficiency monitoring. This work also provides a workflow to clarifythe steps of a typical real-time locating system project, including the cleaning, pre-processing, andanalysis of the data to provide a guideline and reference for research and development of indoorpositioning-based manufacturing solutions.
Industrial Internet of Things based Cycle Time Control of Assembly Lines
Industrial Internet of Things based Cycle Time Control of Assembly Lines
Dynamic cycle time setting and line balancing are the most significant problems in modular manufacturing. Industry 4.0 and IIoT (Industrial Internet of Things) based production management systems connect decentralized production units and information sources to increase productivity and flexibility. We developed an IIoT based solution to ensure a real-time connection between products and assembly lines. The proposed dynamic cycle time setting algorithm takes into account the varying complexity of the product based on the real-time information provided by smart wireless sensors and an Indoor Positioning System (IPS). In this paper, we overview Industry 4.0 based assembly line management solutions, present the developed IIoT based infrastructure, and demonstrate the applicability of the proposed cycle time setting algorithm in a simulation example motivated by an industrial open station conveyor.
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