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AIoT predictive Analytic for Smart Manufacturing

predictive analytic in smart manufacturing

There were several discussion that touched about deep learning AI  and Industrial IOT in manufacturingPredictive analytic in Smart Manufacturing is the focus point in the Live chat in AIoT presented by Axiomtek Malaysia on the 12th of April.  

Why Predictive analytics is crucial in making sure the production plant is in smooth running condition? 

Predictive analytics plays a crucial role in ensuring the smooth running of production plants for several reasons:

Early Detection of Issues: Predictive analytics allows for the early detection of potential issues or failures in machinery or processes by analyzing historical data, sensor readings, and other relevant information. By identifying problems before they escalate, production downtime can be minimized or even prevented altogether.

Optimized Maintenance Scheduling: By analyzing data on equipment performance and wear and tear, predictive analytics can help optimize maintenance schedules. Instead of relying on fixed schedules or reactive maintenance, which can lead to unnecessary downtime or unexpected breakdowns, predictive analytics enables maintenance to be performed when it’s actually needed, reducing downtime and maximizing productivity.

Improved Asset Performance: Predictive analytics can also be used to optimize asset performance by identifying opportunities for process improvements or equipment upgrades. By analyzing data on equipment efficiency, output quality, and other performance metrics, production plants can make data-driven decisions to optimize their operations and maximize output.

Cost Reduction: By reducing unplanned downtime, optimizing maintenance schedules, and improving asset performance, predictive analytics can help production plants reduce costs associated with maintenance, repairs, and lost productivity. This can lead to significant cost savings over time and improve the overall profitability of the plant.

Enhanced Safety: Predictive analytics can also contribute to enhanced safety in production plants by identifying potential safety hazards or risks before they lead to accidents or injuries. By analyzing data on equipment performance, environmental conditions, and other factors, production plants can proactively address safety concerns and implement measures to mitigate risks.

Overall, predictive analytics enables production plants to operate more efficiently, reduce costs, and improve safety by leveraging data-driven insights to anticipate and address potential issues before they impact operations.

predictive analytic with AIOT deep dive

There are several AI models and techniques commonly used in predictive analytics. Some of the most popular ones include:

  1. Linear Regression: Linear regression is a statistical method used to analyze the relationship between two or more variables. It is often used for predictive modeling when the relationship between the variables is linear.

  2. Logistic Regression: Logistic regression is a statistical method used for predicting the probability of a binary outcome based on one or more predictor variables. It is commonly used in classification tasks.

  3. Decision Trees: Decision trees are a popular machine learning technique used for both classification and regression tasks. They work by recursively partitioning the data into subsets based on the value of different predictor variables.

  4. Random Forest: Random forest is an ensemble learning technique that combines multiple decision trees to improve predictive accuracy and reduce overfitting. It works by training a large number of decision trees on different subsets of the data and averaging their predictions.

  5. Gradient Boosting Machines (GBM): Gradient boosting machines are another ensemble learning technique that combines multiple weak learners (typically decision trees) to create a strong predictive model. GBM works by iteratively fitting new models to the residuals of the previous models.

  6. Neural Networks: Neural networks are a class of machine learning models inspired by the structure and function of the human brain. They consist of interconnected nodes organized into layers and can be used for a wide range of predictive analytics tasks, including classification, regression, and time series forecasting.

  7. Support Vector Machines (SVM): Support vector machines are a supervised learning technique used for classification and regression tasks. They work by finding the hyperplane that best separates the data into different classes or predicts the target variable.

  8. Time Series Models: Time series models are specifically designed for analyzing and forecasting time series data, which consists of observations collected at regular intervals over time. Popular time series models include ARIMA (AutoRegressive Integrated Moving Average) and SARIMA (Seasonal ARIMA).

These are just a few examples of the many AI models and techniques available for predictive analytics. The choice of model depends on factors such as the nature of the data, the specific predictive task, and the desired level of accuracy and interpretability.

Axiomtek Malaysia (AXMY) emerges as the premier provider of industrial computer systems and a leading AIoT partner in Malaysia.
As a key player in the Southeast Asian industrial computing and embedded systems sector, AXMY operates under a franchised partner program with Axiomtek Taiwan. This collaboration ensures swift local support and specialized expertise, enabling AXMY to deliver customized solutions across diverse industrial computing domains.
Dedicated to innovation, we continually develop cutting-edge devices and solutions in the AIoT realm, making them accessible and cost-effective for various industries. Our commitment extends to sharing the latest technological advancements in industrial transformation, catering to all levels of expertise within the context of Industry 4.0 and digital transformation initiatives.
To watch this session in our Youtube channel :