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Predictive maintenance feeds real-time data to systems that undertake automated monitoring in order to maintain asset health and notify factories when maintenance is required beforehand. Data is collected over time in scenarios to monitor the state of the equipment. The basic idea is to uncover certain hidden trends that can aid in the prediction and eventual prevention of failures in the machinery.
Predictive Maintenance has been claimed to have a widespread application in market by 2025 with an increase of 25% CAGR. The use of ML in maintenance helps detect and counter equipment/system failures way in advance to help companies be ready. By implementing predictive maintenance, manufacturers can save on down time that can be caused due to a possible failure, and also helps in maintaining continuous manufacturing and a supply chain flow ultimately leading to customer and stakeholder satisfaction.
Predictive maintenance makes use of predictive analytics and ML supervised detection, classification approaches on huge datasets, predicting whether equipment will fail to operate in future or not. The data at hand must consist of previous machine failures, maintenance history, warranty and expiry data, operating conditions, etc.
ANAI provides manufacturers with an opportunity to conduct predictive maintenance in a very efficient no code approach. ANAI’s automated pipeline, starting from data ingestion to deploying models makes it easier for companies to implement such AI-based solutions and helps in quick and informed decision making without the expertise and talent required for ML.
Decreased costs due to factors such as:
In this implementation, we will use ANAI’s automated data engineering pipeline on Predictive maintenance data. Using ANAI, any user can process the data to make it ready for model building and other ML applications. The goal of this step is to transform data into an easily interpreted form for the ML model and hence making it easy for the model to make further predictions. But for the 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 pipeline automates the data pre-processing pipeline. It provides 100 plus data ingestion methods to give a flexibility while importing data, conducts thorough pre-analysis of data to understand its features and distribution, and then proceeds over to data wrangling wherein the missing and the duplicate values are dealt with. For feature engineering, ANAI applies automated feature engineering to detect the features that affect the prediction the most and summarizes the data giving it a final health score.
Steps taken by ANAI for Data Pre-Processing, Engineering and Analysis:
2. Select the data source to which your data belongs to and import it into the ANAI platform.
3. Prepare the data set by selecting the data, selecting the fields that should be considered, the target variable and finally the type of ML task that is needed to be done.
4. Save the project and finally get a summary for the data with a health score and understand the inconsistencies within the data such as missing and duplicate values, etc. In the Overview section.
5. Within the Profile section, you can see various feature statistics for each and every feature that can be saved as a PDF if required.
6. Interactions section provides a detailed analysis by displaying uni-variate and bi-variate analysis of features that helps users understand the data even better and draw key inferences from it.
Exploratory Data Analysis on the predictive maintenance dataset lets manufacturers understand the events which lead to equipment failure, and one of such methods is to analyze features and their correlations on the target variable i.e. it failed or not. ANAI allows visualizing the data to perform churn analysis in order to analyze KPI (Key Performance Indicators) such as Torque, Tool wear, etc. Various types of visualizations, from histograms to pair and scatter plots, can be generated to extract deeper meaning from the data using ANAI. Some of them have been used below for the analysis of Predictive maintenance data.
The pie chart represents distribution of Type feature which represents product quality variants, pie is divided into three segments L (Low: 50% of all products), M (Medium: 30% of all products), H (High: 20% of all products), L (Low) has the highest distribution and H (High) has the lowest distribution.
This chart represents a scatter distribution of rotational speed with torque and they have a negative correlation between them with greater density towards the start and later decreasing across the graph.
This chart represents a scatter distribution of rotational speed with process temperature with the Failure Type as the differentiator. Various failures can be analyzed as for when they take place.
Density Contour represents a torque versus process temperature distribution with target as the differentiator. Class 1(i.e. failing) can mostly be seen for higher torques and vice versa.
This chart represents a correlation diagram for different features and shows there dependency on each other.
The next step for implementing the predictions for predictive maintenance model is to create an ML pipeline to build, train and tune a suitable model that can predict the churn accurately. ANAI’s automated model building tool helps users select the best models for their use case and gives out a comparative analysis for different model’s performance and also automatically tunes the hyperparameters for the best accuracy on the test data set.
The models can be selected based on their simplicity and interpretability depending on the use case so that every stakeholder can understand the model’s working. The most accurate model gives out the best performance while predicting the machinery failure that will result in downtime, with accurate predictions.
We have trained a batch of ML models, keeping “Failure Type” as the target variable from the data set. Below is the after training results for each of the models, with normal accuracies and cross-validated accuracies (after cross validation and hyperparameter tuning) mentioned.
These models have been trained to output a class from a selection of classes available in that target. Based on the initial parameters passed to a model selected (depending on various factors such as accuracy and interpretability), a particular class will be outputted to give out the type of failure that will be caused.
For the second experimentation, we tried training ML models, keeping “Target” as the target variable from the data set. Below is again the performance results for each of the models, with normal accuracies and cross-validated accuracies (after cross validation and hyperparameter tuning) mentioned.
The models trained here can predict if the equipment would fail when certain parameters are reached, and based on this output manufacturers can identify accurately if the machinery is under requirement for a maintenance or not.
ANAI’s eXplainable AI-based solutions, allows 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, LIME, CERTIFAI, and a lot more, to give accurate 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 are the LIME explanations on both types of trainings that we did on the data set. One shows the local explanations for the class “No Failure” and gives out the features that affect that class the most in both incrementing and decrementing ways. The other diagram shows local explanations for class1 i.e. “1” or whether the equipment failed. Here, again how various features affect the predictions have been shown.
Such explanations are helpful in understanding the black boxes of the ML models and helps stakeholders and customers trust the model’s results as they can know for sure that the model is not biased or faulty. Explainable AI techniques opens a road full of opportunities for bringing in transparency, interpretability, explanations and robustness within the entire ML system to help build AI that is more responsible and fair.
According to the reports of Marketsandmarkets, AI in the manufacturing market is expected to grow from USD 1.0 billion in 2018 to USD 17.2 billion by 2025, at a CAGR of 49.5% during this period. AI is now finding its niche in manufacturing, as the technology matures and as costs drop. Manufacturers are also realizing the massive impact this will have. In a recent study by Cognizant’s Center for the Future of Work manufacturing execs stated that AI would have the biggest expected impact of new technologies by 2025, fueling a whopping 395% of growth.
Data gathering, administration, and intelligent usage underpin Predictive Maintenance. This strategy focuses on “asset health,” providing maintenance and repairs depending on the asset’s failure to fulfill specified performance goals. Predictive maintenance using ANAI is intended to assist in determining the state of in-service equipment and predicting when maintenance should be conducted.
Predictive Maintenance enables safety compliance, proactive remedial measures, and extended asset life. Pre-emptive investigations, maintenance schedule changes, and repairs may be undertaken before the asset fails by looking ahead and understanding what failure is likely to occur when. This can be executed by referring to predicted results from ANAI.
Some long terms effects of implementing predictive maintenance can lead to long-term benefits that are crucial for the growth and scalability of business:
According to McKinsey, AI-driven predictive maintenance can boost asset productivity by up to 20 percent while lowering maintenance costs by up to 10 percent. The adoption of data-driven predictive maintenance approaches cuts the yearly operating expenses of predictive maintenance procedures even more.
Here, we discussed how predictive maintenance can be carried out on the ANAI platform with detailed explanation of procedures, from ingesting complex data, to data analysis and feature engineering, to building and tuning ML models. We also discussed the importance of predictive maintenance and the advantages in doing so. In order to reduce the downtime and extra costs in manufacturing, industry data can be analysed using the visualization tools available in the ANAI suite to find certain prerequisites for failure and keeping an eye out for them using ML.
Using ANAI, companies can make quick decisions, and focus more on the business side, implementing solutions to eliminate the problem causing losses, on the go using advanced ML techniques and insightful exploratory data analysis without the live ML expertise needed. ANAI holds simplicity, efficiency, accuracy in mind to provide descriptions, detailed analysis and accurate predictions. ANAI also helps stakeholders with ML prediction understanding and assists in building models that are trustable, fair 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/