By Seth Deland, Product Marketing Manager, Data Analytics, MathWorksFor data scientists, predictive maintenance has several promising outcomes, including reducing machine downtime and avoiding unnecessary maintenance costs while adding revenue streams for equipment vendors with aftermarket services..However, engineers and scientists face challenges around process and data when incorporating predictive maintenance technology into their companies’ operations.Below are four of the most common implementation obstacles that engineers and scientists should avoid when looking to bring predictive maintenance into their organizations.Obstacle 1: Being Unaware of How to Do Predictive MaintenanceWorking with any new technology requires justifiable investment, and predictive maintenance is no exception..© 1984–2018 The MathWorks, Inc.Obstacle 2: Lacking Data to Create Proper Predictive Maintenance SystemsBecause predictive maintenance relies on machine learning algorithms, enough data must exist to create an accurate model..Model success depends on how data is logged: preferably, machines will include logging options that can be modified to record more data, or simulation tools can be used to combine simulated data with available sensor data to build and validate predictive maintenance algorithms.Engineers should avoid a condition where their systems operate in a “feast or famine” mode where little or no data is collected until a fault occurs..© 1984–2018 The MathWorks, Inc.Obstacle 3: Lacking Failure Data to Achieve Accuracy Failure data is a fundamental element of predictive maintenance..They should keep things small, validate against data, and iterate until they are confident with their results.Obstacles aside, data scientists and engineers can take solace in realizing that predictive maintenance is an achievable goal if they can locate the best balance of tools and guidance.. More details