Introduction to Predictive Maintenance Last Updated : 12 Jul, 2025 Comments Improve Suggest changes 2 Likes Like Report Predictive Maintenance is the mechanism performed to prevent faults from occurring, parts adjustments, parts cleaning and parts replacement. Using predictive maintenance, the life of machine, animal or any entity can be predicted. Certain measures need to taken according to the data gathered from various condition monitoring sensors and techniques. Predictive maintenance can be achieved through : Planned Maintenance: Planned maintenance needs a lot of human intervention and monitoring. If the machine fails, it impacts the business economically. Recognising Anomalous Behaviours: With the help of certain machine learning techniques and deep learning algorithm anomaly can be detected which forms the backbone of predictive maintenance. Human intervention in this process is very less. To the given data set, the prediction is done and certain measures are to be taken accordingly. How Does Predictive Maintenance work? Predictive maintenance relies on condition-monitoring equipment’s which helps to assess the performance of assets in real-time. With the use of predictive formulae and Internet of Things (IoT), predictive maintenance creates an accurate tool for collecting and analysing asset data. Condition-monitoring equipment: Under predictive maintenance, each asset is monitored using conditioned monitoring equipment. Specifically, the machines are fitted with sensors that capture data about the equipment to enable evaluation of the asset’s efficiency. Because of this step, the traditional way of physical monitoring of assets can be reduced. These sensors measure different kinds of parameters depending on the type of machine. They measure vibration, noise, temperature, pressure etc. The Internet of Things: It is one of the tools to gather data. Different sensors in the device help in the collection and sharing of data. Predictive maintenance depends on these sensors which connect the assets to a central system and stores the information. Anomaly Detection: It is the identification of events that do not conform to the expected pattern. It will be different from the pattern. For example - detecting anomalies in heart beats, detecting machine part failures. Create Quiz Comment R RachnaShukla Follow 2 Improve R RachnaShukla Follow 2 Improve Article Tags : Machine Learning Explore Machine Learning BasicsIntroduction to Machine Learning8 min readTypes of Machine Learning7 min readWhat is Machine Learning Pipeline?6 min readApplications of Machine Learning3 min readPython for Machine LearningMachine Learning with Python Tutorial5 min readNumPy Tutorial - Python Library3 min readPandas Tutorial4 min readData Preprocessing in Python4 min readEDA - Exploratory Data Analysis in Python6 min readFeature EngineeringWhat is Feature Engineering?5 min readIntroduction to Dimensionality Reduction4 min readFeature Selection Techniques in Machine Learning6 min readSupervised LearningSupervised Machine Learning7 min readLinear Regression in Machine learning15+ min readLogistic Regression in Machine Learning11 min readDecision Tree in Machine Learning8 min readRandom Forest Algorithm in Machine Learning5 min readK-Nearest Neighbor(KNN) Algorithm8 min readSupport Vector Machine (SVM) Algorithm9 min readNaive Bayes Classifiers7 min readUnsupervised LearningWhat is Unsupervised Learning5 min readK means Clustering – Introduction6 min readHierarchical Clustering in Machine Learning6 min readDBSCAN Clustering in ML - Density based clustering6 min readApriori Algorithm6 min readFrequent Pattern Growth Algorithm5 min readECLAT Algorithm - ML5 min readPrincipal Component Analysis(PCA)7 min readModel Evaluation and TuningEvaluation Metrics in Machine Learning9 min readRegularization in Machine Learning5 min readCross Validation in Machine Learning5 min readHyperparameter Tuning7 min readML | Underfitting and Overfitting5 min readBias and Variance in Machine Learning6 min readAdvanced TechniquesReinforcement Learning9 min readSemi-Supervised Learning in ML5 min readSelf-Supervised Learning (SSL)6 min readEnsemble Learning8 min readMachine Learning PracticeMachine Learning Interview Questions and Answers15+ min read100+ Machine Learning Projects with Source Code [2025]6 min read Like