IoT-based data-driven predictive maintenance relying on fuzzy system and artificial neural networks
Ashraf Aboshosha1, Ayman Haggag2, Neseem George3,2
1Rad. Eng. Dept., NCRRT, Egyptian Atomic Energy Authority (EAEA), Cairo, Egypt. ashraf.aboshosha@eaea.org.eg.
Scientific Reports
|July 27, 2023
Summary
This study introduces a data-driven predictive maintenance framework using AI, IoT, and Sensors Information Modeling to enhance industrial machine upkeep. The approach minimizes human error in fault recognition, improving production line efficiency.
Area of Science:
- Industrial Engineering
- Artificial Intelligence
- Manufacturing Systems
Background:
- Industry 4.0 necessitates advanced maintenance strategies beyond reactive and preventive maintenance (PM).
- Current maintenance management faces challenges in efficiency and human-based fault recognition errors.
- Integrating advanced automation and AI is crucial for optimizing industrial production lines.
Purpose of the Study:
- To develop and validate a data-driven predictive maintenance (PdM) planning framework for industrial production lines.
- To leverage AI, IoT, and Sensors Information Modeling (SIM) for improved maintenance management.
- To minimize human error in fault recognition and enhance overall production efficiency.
Main Methods:
- Implementation of a predictive maintenance (PdM) framework utilizing AI, IoT, and SIM.
- Application of Deep Learning (DL) for alarming and fault diagnosis.
- Utilization of Fuzzy Logic System (FLS) for AI-based preventive maintenance (PM).
- Practical validation on a corrugated cardboard production factory.
Main Results:
- Demonstrated feasibility of the proposed data-driven predictive maintenance framework in a real industrial environment.
- Successful interpretation of alarming patterns into specific faults using Deep Learning (DL).
- Improved efficiency in industrial production machine maintenance management through SIM and IoT integration.
Conclusions:
- The proposed framework offers a superior maintenance strategy for Industry 4.0 environments.
- AI-driven predictive maintenance significantly reduces human-based fault recognition errors.
- Integration of SIM and IoT enhances the efficiency and reliability of industrial maintenance.
Related Concept Videos
01:10Control Systems
1.2K
Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
At the heart...
1.2K
01:26Feedback control systems
347
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
347
01:25Multimachine Stability
192
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
192
01:26Classification of Systems-I
215
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
215
01:25Distribution Reliability and Automation
133
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
133
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
81
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
81