When it comes to equipments, failures are inevitable. These failures can be caused by various factors such as mechanical wear and tear, manufacturing defects, or even external factors like power surges or natural disasters. The cost of equipment failures can be significant, both in terms of data loss and downtime, leading to revenue losses for businesses.
This is where predictive maintenance comes into play. Predictive maintenance is the use of data and analytics to monitor the performance of equipment and predict when maintenance is required. In the case of hard drives, this means monitoring the health of the drive and predicting when it is likely to fail, so that it can be replaced or repaired before it causes any damage.
Machine learning is a key component of predictive maintenance, as it enables the analysis of large amounts of data and the identification of patterns and trends that can indicate potential failures. However, implementing machine learning traditionally can be a daunting task, requiring significant infrastructure and technical expertise.
Octai offers a no-code solution to this problem, allowing businesses to leverage machine learning without the need for significant infrastructure or technical expertise. Octai is a cloud-based platform that provides pre-built models and tools for building and deploying machine learning models. With Octai, businesses can quickly and easily build predictive maintenance models for their equipments, without the need for extensive coding or infrastructure.
In this tutorial, we will explore a real-life scenario of predicting hard drive failures. This involves working with one of the largest real datasets in the field of predictive maintenance, which is available on Kaggle. You can find more details about this dataset by visiting the following link:https://www.kaggle.com/datasets/backblaze/hard-drive-test-data
Let's jump into the solution with Octai.
We can start by uploading data. Simply click "Add Data" and select Local file to connect the dataset you obtained from the Kaggle link.
Dataset contains date, basic hard drive information and 45 different S.M.A.R.T. status statistics. S.M.A.R.T. (Self-Monitoring, Analysis, and Reporting Technology) status of a hard disk refers to a monitoring system that is built into most modern hard drives to provide information about the health and performance of the drive. S.M.A.R.T. is designed to detect and report on various indicators or attributes of a hard drive that may indicate potential or imminent failure, allowing users to take appropriate action before data loss occurs.
Our dataset contains failure records as failure column being 0 or 1. We wan't to predict if the hard drive will fail tomorrow, so we will shift these values by taking leads of it. To do that feature engineering step, simply drag n drop "Feature Flow" node and click on it.
Within the Feature Flow, drag the 'Failure' column and connect it to the 'Shift' node. Group the data by serial number, and select a shift value of -1 to shift the values in the column. This will create a new column that corresponds to the next occurrence of failure for the same hard drive with the same serial number, i.e., the next day's failure value. This new column will be the target variable for our predictions.
We have information on the models of the hard drives, and in order to use this information with a machine learning model, we can encode the values so that each model has its own unique integer value. To achieve this, we can use the Label Encoder from the "Encoder" node and connect the model column to it.
Our data contains normalized sensor values. To better understand the behavior of the sensors, we can use expanding windows, which are a widely used technique in time-series analysis. This involves aggregating data over increasingly larger time intervals, allowing us to capture long-term patterns and trends in the data that may not be visible in smaller intervals.
By applying expanding windows to sensor value columns, we can analyze historical behavior and determine whether the current reading of the sensor is relevant to its past behavior. This information can be used to identify anomalies or deviations from the expected behavior, which may be indicative of faults or errors in the sensor. This approach is particularly useful in applications where maintaining accurate sensor behavior is critical, such as in industrial automation, environmental monitoring, and medical sensing.
In our specific use case, we are using the sum function to gather information about the total load or accumulated tiredness of the hard drive. However, other methods can also be explored, depending on the specific goals of the analysis. To use expanding windows, we can simply connect the sensor column to Window as below.
After feature engineering, we can go back and run the flow to generate a model-ready dataset, then build machine learning models in the Model section. Our goal is to accurately predict hard drive failure. We can explore different modeling techniques, fine-tune hyperparameters, and evaluate performance using cross-validation or other methods. By applying these techniques, we can gain valuable insights into sensor behavior and make accurate predictions. Luckily, "Model" section in Octai automatically handles these steps.
Simply select the dataset that we performed feature engineering, and the target label you'd like to predict, then Octai will automatically build an accurate ML model. If you wish to get in the details of which metrics to use, how to do validation, hyperparameter space etc., you can click "Select More Parameters and Train" to configure that.
As you can see, Octai can successfully build ML models with AUC score 0.875, by handling all hyperparameter optimizations. You can also list your past experiments and try new ones in this page, or even build ensemble models. It is also possible to deploy your experiments with one click, by mapping the streaming data of sensors to the models, while the rest (data transformations, feature engineering pipelines, hosting the model) is handled by Octai.
In summary, predictive maintenance using machine learning is crucial in minimizing equipment failures and revenue losses for businesses. Octai offers a no-code solution to this problem, allowing businesses to easily build and deploy predictive maintenance models. By following the basic steps outlined in this tutorial, businesses can accurately predict hard drive failures and prevent potential data loss. With Octai, deploying machine learning models has never been easier, allowing businesses to focus on their operations.
Updated 5 months ago