A proactive maintenance strategy that uses data analysis to predict equipment failures before they occur.

A network of interconnected devices that collect and exchange data, crucial for real-time monitoring in predictive maintenance.

A set of processes that automate the movement and transformation of data from source to destination for analysis.

An ensemble learning method that uses multiple decision trees to improve prediction accuracy and control overfitting.

A machine learning technique that builds models sequentially, optimizing for errors made by previous models to enhance performance.

The process of selecting, modifying, or creating features from raw data to improve model performance.

The process of optimizing the parameters of a machine learning model to improve its performance.

Quantitative measures used to assess the performance of machine learning models, such as accuracy, precision, and recall.

The steps taken to clean and prepare raw data for analysis, including handling missing values and normalizing data.

A plan outlining how a machine learning model will be integrated into production environments for real-time use.

A set of rules that allows different software applications to communicate, commonly used for deploying machine learning models.

The phenomenon where a model's performance degrades over time due to changes in the underlying data patterns.

Processes that allow for continuous improvement of models by incorporating new data and insights.

Metrics used to evaluate the success of a predictive maintenance strategy, such as downtime reduction.

The process of ensuring that data is accurate and usable before it is analyzed or used in modeling.

Techniques used to identify and handle data points that differ significantly from the rest of the dataset.

A technique for assessing how a model will generalize to an independent dataset by partitioning the data into subsets.

Methods for analyzing time-ordered data points to extract meaningful statistics and identify trends.

Identifying unexpected patterns or behaviors in data that do not conform to expected norms.

Methods used to gather data from various sources, crucial for building a robust data pipeline.

The process of teaching a machine learning model to make predictions based on a training dataset.

The immediate processing of data as it is collected to enable timely decision-making.

Plans and practices employed to maintain equipment and reduce downtime, enhanced through predictive analytics.

Expertise in a specific field that informs feature selection and model development for relevant applications.