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The influence of machine maintenance on machine downtime and production costs is directly proportional to a manufacturing company’s cost, quality, and performance competitiveness. The goal of maintenance extends beyond fixing malfunctioning equipment. Its primary purpose is to preserve the functionality of equipment and reduce breakdowns. One method of conducting analysis of equipment is by installing sensors, which detect and compute reading generated by essential working components of machinery. Manual checkup of data, machinery piles up load due to downtime and can lead to human errors, with advancing computing power and technologies using machine learning, the model can be trained on sensors data, with the ability to predict outliers or defects that may lead to downtime of equipment. Integral functions in manufacturing industries using such technology can conduct smooth operations.
ANAI uses optimization techniques to frame data to be ML proficient, using data generated by sensors, ANAI has completely automated processes ranging from data ingestions, data engineering, feature engineering, model building, and predictions, explaining predictions (XAI). Predictions will help facilitate consistent and cost-effective inspections. Intelligent and predictive inspections of machinery will result in good quality products and services.
How ANAI’s Predictive Powers can be put to use:
ANAI helps process and transform equipment data to make it ready for model building and other ML applications, the purpose being to transform data that can be easily interpreted by ML model and hence making it easy to make predictions. For data to be ML-ready it needs to satisfy certain conditions and meet requirements, hence the data undergoes a few pre-processing steps like data analysis, wrangling, transformation, encoding, etc.
ANAI’s Data Engineering feature automates the data preprocessing pipeline, wherein it implements ML-based Data Wrangling which covers various aspects of preprocessing including
For this case study on ANAI, we selected a highly complicated data set that contained multiple files on the imbalances for an induction motor shaft. ANAI was able to easily manage this data that was simulated with load values within the range from 6 g to 35 g.
Feature selection, one of the key processes when it comes to data engineering, is completely automated by ANAI. Selecting the best possible features correlating with the target variable, machinery fault data contains various features or attributes which depict and correlate with the smooth working of the equipment. These features are essential in determining possible imbalance faults, horizontal and vertical misalignment faults, and inner and outer bearing faults. Selecting the optimal features is needed to accurately predict maintenance and repair to reduce downtime and liabilities. ANAI has the ability to perform feature selection and feed features that are highly correlated to the ML model for accurate predictions.
ANAI using AutoML can analyze customer data and identify recurring patterns across a variety of features. Predictions from ANAI can often assist organizations to identify and combat defects beforehand that would be difficult to identify using intuition and manual data analysis. Also, can help assist in key decision-making for management-related activities. The next step for implementing the predictions for the machinery fault detection model is to create an ML pipeline to build, train and tune a suitable model that can predict the defects accurately. ANAI’s automated model building tool helps users select the best models for their use case and gives out a comparative analysis of different models’ performance and also automatically tunes the hyperparameters for the best accuracy on the test data set.
(CatBoost Classifier proved to be an optimal algorithm for making accurate predictions by achieving the highest accuracy of 99.89%)
ANAI’s eXplainable AI-based solutions, allow businesses to get deeper within the model’s results and uncover the black box of the ML models. ANAI provides various eXplainable AI models such as SHAP, LOFO, CERTIFAI, etc. to give explanations on the model’s output so that every stakeholder can properly understand the reasoning behind a model’s prediction and the model creators can quickly detect biases and other discrepancies beforehand.
Below can be seen one of such XAI techniques called LIME. LIME generates local explanations for a black box model and helps its respective users with understanding the feature dependency of black-box model on various features it is trained on.
Here, the model is heavily relying on feature_14 and feature_15 for its predictions.
Above, can be seen another method to get explanations from model results. This method is termed as SHAP value which tries to give the contribution for each of its features for making the prediction. These plots can also be plotted for each of the individual features as can be seen below.
The last XAI method that we used for ensuring that the explanations are right and verifiable is Permutation Feature Importance which again shows how the feature_14 and feature_15 affect the predictions the most.
According to industrial manufacturing business leaders, in the next two years, AI technology will have varying impacts and will experience growth dependent on industry needs: 21 percent for product design, development, and engineering; 21 percent for maintenance operations; 15 percent for production/assembly.
Since quality management is one of the most integral functions in a manufacturing process, using AutoML with ANAI can leverage the most beneficial technology for fault detection. After the available data are secured, the data often has to be pre-processed depending on the requirements of the algorithm of choice. Pre-processing of data has a critical impact on the results. However, ANAI automates the pre-processing transformation by applying data engineering and feature selection techniques to develop the best model-building features.
AutoML provides more flexibility since it can quickly grasp differences in location, orientation, lighting, and backdrop textures. Traditional automated procedures make this incredibly challenging. AI-assisted machines will be able to do previously manual jobs much better and that too without any breaks.
ANAI may also outperform manual or conventional flaw detection procedures in terms of accuracy. ANAI may be used by industries that seek to incorporate a well-designed defect detection model that can give quicker, more accurate results and high-quality goods to customers. AI-assisted visual inspection machines were also shown to detect faults with around 90% greater accuracy than a human and can do it in around the time it takes to blink (0.5 of a second).
86% of CEOs acknowledged that AI was crucial to success. But only 30 of them have been able to scale AI and other emerging technologies to drive business value. Because ANAI’s AutoML models are very scalable and flexible, industries may incorporate them at earlier stages of production, enabling faulty components to be rejected earlier in the manufacturing process. This strategy of identifying flaws early in the product life cycle saves cost by preventing a damaged product from going through other unnecessary procedures in the manufacturing pipeline.
This case study covers the need for fault detection in machinery, and the advantages and solutions offered by ANAI to tackle these faults. It also provides analysis and explanations of the models incorporated by ANAI to reach the objective. The automated process from basic data importing to predicting defects with maximum accuracy and optimal features.
Using ANAI as a tool, organizations can make a quick, effective decision for equipment operations-related activities, using solutions to eliminate any possible liabilities. Insightful, accurate, and detailed predictions, are features important that affect machines’ health. ANAI helps understand model results and assists in building models that are trustable, fair, scalable, and robust.
To implement such solutions or to get a personalized solution for your niche use case, contact us at firstname.lastname@example.org or visit www.anai.io/contact/
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