A company has a fleet of devices transmitting daily sensor readings. They would like to create a predictive maintenance solution to proactively identify when maintenance should be performed. This approach promises cost savings over routine or time based preventive maintenance, because tasks are performed only when warranted.

The task is to build a predictive model using machine learning to predict the probability of a device failure. When building this model, be sure to minimize false positives and false negatives. The column you are trying to Predict is called failure with binary value 0 for non-failure and 1 for failure.

Data Description: This dataset contains all information on work orders completed for assets from January 2008 to present. This includes but it not limited to type of work completed, date stamps of all repair and maintenance milestones, all costs associated with the work order, amount of labor completed and where the work orders were completed.

Data Creation: Data is recorded for all maintenance and repairs of the City’s vehicle fleet

Data Created By: Fleet Services Division of the Public Services Department

Refresh Frequency: Daily

Data Dictionary: A data dictionary providing definitions of columns and attributes is available as an attachment to this dataset.

Processing: The City of Cincinnati is committed to providing the most granular and accurate data possible. In that pursuit the Office of Performance and Data Analytics facilitates standard processing to most raw data prior to publication. Processing includes but is not limited: address verification, geocoding, decoding attributes, and addition of administrative areas (i.e. Census, neighborhoods, police districts, etc.).

Data Usage: For directions on downloading and using open data please visit our How-to Guide: https://data.cincinnati-oh.gov/dataset/Open-Data-How-To-Guide/gdr9-g3ad

Public (anonymized) predictive maintenance datasets from Huawei Munich Research Center.

Datasets from a variety of IoT sensors for predictive maintenance in elevator industry. The data is useful for predictive maintenance of elevators doors in order to reduce unplanned stops and maximizing equipment life cycle.

The dataset contains operation data, in the form of timeseries sampled at 4Hz in high-peak and evening elevator usage in a building (between 16:30 and 23:30). For an elevator car door the system we consider: Electromechanical sensors (Door Ball Bearing Sensor), Ambiance (Humidity) and Physics (Vibration).

Preventive maintenance program is the most commonly deployed maintenance strategy in the manufacturing industry worldwide in 2021. As of 2021, ** percent of the respondents reported following a preventive maintenance strategy, while ** used reactive maintenance (run-to-failure).

This dataset is designed for developing predictive maintenance models for industrial equipment. It includes real-time sensor data capturing key operational parameters such as temperature, vibration, pressure, and RPM. The goal is to predict whether maintenance is required based on these metrics. The data can be used for anomaly detection, predictive maintenance modeling, and time series analysis.

Columns:

Timestamp: The date and time when the data was recorded. Temperature (°C): Temperature of the equipment in degrees Celsius. Vibration (mm/s): Vibration level measured in millimeters per second. Pressure (Pa): Pressure applied to the equipment in Pascals. RPM: Rotations per minute of the equipment. Maintenance Required: A binary indicator (Yes/No) showing whether maintenance is needed, based on predictive modeling. Usage: This dataset is ideal for machine learning enthusiasts and professionals interested in predictive maintenance and industrial IoT applications.

This synthetic dataset is modeled after an existing milling machine and consists of 10 000 data points from a stored as rows with 14 features in columns

Preventive Maintenance Solution Market Outlook

The global preventive maintenance solution market size was valued at approximately USD 5.5 billion in 2023 and is anticipated to grow to USD 10.8 billion by 2032, registering a remarkable compound annual growth rate (CAGR) of 8.0% during the forecast period. This market's growth is driven by the increasing need for efficient maintenance practices and the adoption of advanced predictive technologies in various industries. The integration of IoT and AI into maintenance systems has substantially enhanced the operational efficiency, leading to an increased demand for preventive maintenance solutions. As industries strive to minimize downtime and reduce operational costs, the focus on preventive maintenance continues to gain traction, further propelling market expansion.

The growth of the preventive maintenance solution market is primarily fueled by the rising awareness among industries about the benefits of reducing equipment downtime and enhancing operational efficiency. Organizations are increasingly realizing that traditional reactive maintenance approaches are neither cost-effective nor sustainable in the long run. The transition towards predictive and preventive maintenance strategies enables companies to identify potential equipment failures before they occur, thus minimizing unscheduled downtimes and maintenance costs. Advances in sensor technologies and data analytics have made it possible to continuously monitor equipment health, thereby facilitating timely interventions. This technological evolution is a significant growth factor, as it empowers industries across sectors to optimize their maintenance operations.

Moreover, the proliferation of IoT devices and the increasing accessibility of cloud computing resources are pivotal growth drivers for the preventive maintenance solution market. IoT devices provide real-time data that can be used to monitor the condition of machinery and equipment continuously. This data, when processed using sophisticated algorithms and analytics tools, can predict equipment failures well in advance. The cloud-based deployment of preventive maintenance solutions further aids in scalability and flexibility, allowing businesses of all sizes to implement these solutions without the need for extensive infrastructure investments. The synergy between IoT and cloud computing thus acts as a catalyst in the expansion of preventive maintenance solutions, making them accessible and applicable to a broader range of industries.

Furthermore, the burgeoning emphasis on cost efficiency and competitiveness in industries such as manufacturing, energy, and transportation propels the demand for preventive maintenance solutions. These sectors operate on thin margins and face intense competition, necessitating strategies that cut down on unnecessary costs while maximizing productivity. Preventive maintenance solutions help achieve these objectives by extending the lifecycle of machinery, ensuring continuous production, and reducing unexpected maintenance expenditures. The ability to maintain equipment reliability and efficiency directly translates into competitive advantages for businesses, making preventive maintenance a strategic imperative across various sectors.

The implementation of Predictive Maintenance PdM Software has become a game-changer for industries aiming to enhance their maintenance strategies. By leveraging advanced analytics and machine learning algorithms, PdM software enables organizations to predict potential equipment failures before they occur, thus minimizing unplanned downtimes. This proactive approach not only extends the lifespan of machinery but also optimizes maintenance schedules, ensuring that resources are utilized efficiently. As industries continue to adopt IoT and AI technologies, the integration of PdM software into their operations becomes increasingly seamless, offering real-time insights into equipment health and performance. This technological advancement is pivotal for businesses seeking to maintain a competitive edge by reducing operational costs and improving overall productivity.

Regionally, the demand for preventive maintenance solutions is burgeoning as industries across the globe recognize the cost-saving potential and efficiency gains offered by these systems. North America currently holds a significant share of the market, driven by the high adoption rate of advanced technologies and a well-established industrial base. Europe follows closely, with countries like Germany and the U.K. prioritizing in

The global Data Center Preventive Maintenance Services market is experiencing robust growth, driven by the increasing adoption of cloud computing, the proliferation of data centers, and the escalating demand for high uptime and data security. The market's expansion is further fueled by stringent regulatory compliance requirements and the rising awareness of the long-term cost benefits associated with proactive maintenance over reactive repairs. Key segments within the market, such as equipment maintenance and environmental maintenance, are showing particularly strong performance, reflecting the crucial role these services play in ensuring optimal data center functionality. The significant investments in data center infrastructure across North America and Europe, particularly in the United States, United Kingdom, and Germany, are contributing significantly to market growth. However, factors such as the high initial investment costs for implementing preventive maintenance programs and the potential for skilled labor shortages represent potential restraints to growth. We estimate the market size in 2025 to be approximately $15 billion, with a Compound Annual Growth Rate (CAGR) of 8% projected through 2033. This growth reflects a considerable increase in the demand for reliable data center operations across various industries, including finance, manufacturing, and the government sector. Leading market players are actively focusing on strategic partnerships, technological advancements (like AI-driven predictive maintenance), and geographic expansion to capitalize on this expanding market. The diverse range of services offered within the data center preventive maintenance sector caters to the specific needs of various industry verticals. Financial institutions and insurance companies show a high demand for reliable data center uptime due to the critical nature of their operations. Similarly, the manufacturing and government sectors are increasingly reliant on robust data infrastructure to support their operational efficiency and critical functions. The regional distribution of the market reflects the concentration of data center infrastructure in developed economies, with North America and Europe holding significant market share. However, emerging economies in Asia-Pacific, particularly China and India, are showing rapid growth potential as their data center infrastructure expands to support their burgeoning digital economies. The forecast period reveals a continued upward trajectory, indicating a substantial opportunity for existing and new players in this rapidly evolving market.

Preventive maintenance work 2016-2020

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The industrial predictive maintenance market share in APAC is expected to increase by USD 7.44 billion from 2021 to 2026, and the market’s growth momentum will accelerate at a CAGR of 34.71%.

This industrial predictive maintenance market in APAC research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers the industrial predictive maintenance market in APAC segmentation by End-user (oil and gas, chemical and petrochemical, aerospace and defense, power generation, and others), deployment (cloud and on-premises), and geography (China, Japan, India, and Rest of APAC). The industrial predictive maintenance market in APAC report also offers information on several market vendors, including General Electric Co., Huawei Investment and Holding Co. Ltd., International Business Machines Corp., Oracle Corp., Robert Bosch GmbH, SAP SE, SAS Institute Inc., Siemens AG, Splunk Inc., and TIBCO Software Inc. among others.

What will the Industrial Predictive Maintenance Market Size in APAC be During the Forecast Period?

Download the Free Report Sample to Unlock the Industrial Predictive Maintenance Market Size in APAC for the Forecast Period and Other Important Statistics

Industrial Predictive Maintenance Market in APAC: Key Drivers, Trends, and Challenges

The developments in customized industrial predictive maintenance is notably driving the industrial predictive maintenance market in APAC, although factors such as low investments in the latest machinery and measuring equipment may impede market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the industrial predictive maintenance industry in APAC. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

Key Industrial Predictive Maintenance Market Driver in APAC

One of the key factors driving the global industrial predictive maintenance market growth is the developments in customized industrial predictive maintenance. Countries such as China, Japan, and South Korea are leading the automation industry in the region, which is creating opportunities for the growth of the industrial predictive maintenance market in APAC. The rise in the adoption of advanced technologies, such as IoT, industrial IoT (IIoT), AI, and big data, as well as investments in improving product quality and production assets in APAC, are expected to lead to the increased adoption of industrial predictive maintenance during the forecast period. Therefore, vendors such as SAP SE, International Business Machines Corp., and Oracle Corp. provide custom-made industrial predictive maintenance solutions and services based on the needs of specific end-users, which will protect their critical equipment and enable them to gain a competitive edge in productivity.

Key Industrial Predictive Maintenance Market Trend in APAC

Shift from reactive to predictive maintenance is one of the key industrial predictive maintenance market trends that is expected to impact the industry positively in the forecast period. The integration of business information along with sensor data and enterprise asset management (EAM) systems is allowing end-user industries to move away from reactive and shift to predictive maintenance services and solutions. The development of IoT solutions that use real-time machinery data to determine the operational efficiency and condition of the equipment, with the support of sophisticated analytics, helps to predict failures early, unlike preventive maintenance. The disadvantages associated with preventive maintenance are the key factors for the shift to predictive maintenance, as preventive maintenance does not prevent catastrophic failures, is labor-intensive, and needs unnecessary maintenance, which causes damage to equipment and components. Such factors will further support the market growth during the forecast years.

Key Industrial Predictive Maintenance Market Challenge in APAC

One of the key challenges to the global industrial predictive maintenance market growth is the low investments in the latest machinery and measuring equipment. Industrial predictive maintenance requires that the software solutions and services exhibit better performance and have a better impact on industrial assets and production. In addition, there are difficulties in retrofitting existing and older industrial machinery with sensors and monitoring equipment. End-user industries such as oil and gas, chemical and petrochemical, and power generation generally still operate using older machinery, which will, in turn, hamper the adoption of predictive maintenance solutions and services. Moreover, the adoption of industrial predictive maintenance is currently low in develo

Industrial Predictive Maintenance Market Outlook

The global Industrial Predictive Maintenance market size was valued at approximately USD 4.5 billion in 2023 and is projected to reach around USD 15.7 billion by 2032, growing at an impressive CAGR of 14.7% during the forecast period. This remarkable growth is driven by the increasing adoption of Industry 4.0 and the Internet of Things (IoT) in various industrial sectors which have necessitated a shift towards predictive maintenance solutions. These solutions enable companies to predict equipment failures before they occur, leading to reduced downtime and operational costs. The rise in smart factories and the digitalization of industrial processes are further propelling the demand for predictive maintenance technologies.

One of the key growth factors in the Industrial Predictive Maintenance market is the rapid advancement in sensor technologies and the proliferation of IoT devices. As industries seek to improve operational efficiency and reduce maintenance costs, the integration of advanced sensors and IoT devices becomes indispensable. These technologies provide real-time data and analytics that help in predicting equipment failure, thereby preventing sudden breakdowns. Additionally, the drop in prices of sensors and the availability of cloud computing resources make predictive maintenance solutions more accessible and cost-effective for small and medium enterprises, further driving market growth.

Another significant factor contributing to market growth is the increasing focus on reducing operational costs and maximizing asset lifespan. Predictive maintenance techniques, such as vibration monitoring and thermal imaging, allow companies to monitor equipment health continuously. By identifying potential issues before they lead to major failures, organizations can avoid costly repairs and extend the life of critical machinery. This cost-saving potential is encouraging more industries to adopt predictive maintenance solutions. Furthermore, as global competition intensifies, companies are under pressure to maintain a competitive edge, which predictive maintenance can provide by ensuring high levels of equipment efficiency and reliability.

The growing emphasis on sustainability and environmental regulations is also a crucial driver for the Industrial Predictive Maintenance market. Industries are increasingly mindful of their environmental footprint and are adopting sustainable practices. Predictive maintenance plays a vital role in this transition by minimizing waste and energy consumption associated with unexpected equipment failures. Through optimizing maintenance schedules and improving equipment performance, industries can significantly reduce their energy usage and emissions, aligning with global sustainability goals. This alignment with environmental objectives not only aids in regulatory compliance but also enhances corporate reputation, which is increasingly important to modern consumers.

The introduction of a Smart Predictive Maintenance System is revolutionizing how industries approach equipment management. By harnessing advanced algorithms and machine learning, these systems can analyze vast amounts of data to predict potential equipment failures with unprecedented accuracy. This proactive approach not only minimizes downtime but also extends the lifespan of machinery, offering significant cost savings. As industries become more reliant on digital technologies, the integration of smart systems into maintenance strategies is becoming essential. These systems provide real-time insights and actionable recommendations, enabling companies to optimize their maintenance schedules and reduce unexpected disruptions. The shift towards smart predictive maintenance is a testament to the growing importance of data-driven decision-making in industrial operations.

Regionally, North America currently dominates the Industrial Predictive Maintenance market, owing to its advanced technological infrastructure and early adoption of innovative technologies. The presence of key industry players and a strong focus on research and development activities contribute significantly to the region's market dominance. Europe follows closely, driven by stringent regulations for industrial safety and a robust manufacturing sector. However, the Asia Pacific region is expected to exhibit the highest growth rate during the forecast period. This surge is attributed to the rapid industrialization in countries like China and India, where there is a growing need to enhance operational effic

1) Data Introduction • The Predictive Maintenance Dataset is industrial preservation data built for the development of mechanical failure prediction and preservation solutions, including equipment sensor data, operating conditions, and failure (binary value).

2) Utilize data (1) Predictive Maintenance Dataset의 특성: • This dataset contains a variety of operational and status information, including daily sensor measurements for each equipment (e.g., temperature, rotational speed, torque, wear and tear), and failure. (2) Predictive Maintenance Dataset의 활용: • Development of predictive failure prediction model: It can be utilized to build a machine learning-based predictive preservation model that proactively predicts the possibility of machine failure using sensor and operational data. • Improve maintenance efficiency and cost savings: Use failure predictions to ensure timely maintenance to reduce unnecessary maintenance and increase equipment utilization and cost efficiency.

This measure tracks the percentage of lane miles managed by the City of Austin that had preventive maintenance completed. Preventive maintenance of existing pavements extends the life of the surfaces and reduces the amount of capital investment required on City streets each year. Asphalt overlaying, thin surface treatments, and crack sealing existing surfaces are the primary components of what constitutes the City's Preventive Maintenance program.

This dataset supports the measure M.E.3 within the Mobility outcome under the Strategic Direction 2023 initiative.

Data Source: Pavement Management Information System, Street & Bridge Operations

Calculation: Total number of lane miles of street preventative maintenance completed* / Total number of lane miles managed

*Total number of lane miles of street preventative maintenance completed = Lane miles of overlay completed + lane miles of preventative maintenance crack seal completed + lane miles of preventative maintenance thin surface treatments completed.

Measure Time Period: Quarterly (Fiscal Year)

Automated: No

Date if last description update:

No. of Preventive maintenance operations

Maintenance data includes information on maintenance performed on the electric vehicles, including preventive maintenance, service calls, and availability of the vehicles. The parameters collected, and their definitions, will vary due to the differences in maintenance tracking systems that exist between fleets. Parameter definitions are detailed in the data dictionary, and specific vehicle information is available in the vehicle attributes table. Vehicle ID can be used as a key between maintenance data and vehicle attribute tables. Data is being uploaded quarterly through 2023 and subject to change until the conclusion of the project.

description: Annual statistics about operations of the Fleet Maintenance division of the Public Works Department. Data sets include: annual budget figures, total employees, number and type of vehicles and equipment, number of repairs completed, service calls, preventative maintenance activities and more.; abstract: Annual statistics about operations of the Fleet Maintenance division of the Public Works Department. Data sets include: annual budget figures, total employees, number and type of vehicles and equipment, number of repairs completed, service calls, preventative maintenance activities and more.

Predictive Maintenance For Manufacturing Industry Market size was valued at USD 8.26 Billion in 2024 and is projected to reach USD 47.64 Billion by 2032, growing at a CAGR of 24.49% from 2026 to 2032.

Key Market Drivers: Advancements in IoT and Sensor Technology: IoT and sensor technology have transformed data collection and analysis in manufacturing. These technologies provide real-time monitoring of equipment health, including vital factors like temperature, vibration, and pressure. The capacity to collect continuous, high-resolution data enables more accurate predictive maintenance models, which reduces unplanned downtime and optimizes the maintenance schedule. Increasing Adoption of Big Data and Analytics: Manufacturers may now evaluate large amounts of data generated by their machines thanks to the growing adoption of big data analytics. Advanced analytics tools and machine learning algorithms can detect patterns and predict equipment failures with great accuracy.

Context

This an example data source which can be used for Predictive Maintenance Model Building. It consists of the following data:

• Machine conditions and usage: The operating conditions of a machine e.g. data collected from sensors.
• Failure history: The failure history of a machine or component within the machine.
• Maintenance history: The repair history of a machine, e.g. error codes, previous maintenance activities or component replacements.
• Machine features: The features of a machine, e.g. engine size, make and model, location.

Details

• Telemetry Time Series Data (PdM_telemetry.csv): It consists of hourly average of voltage, rotation, pressure, vibration collected from 100 machines for the year 2015.

• Error (PdM_errors.csv): These are errors encountered by the machines while in operating condition. Since, these errors don't shut down the machines, these are not considered as failures. The error date and times are rounded to the closest hour since the telemetry data is collected at an hourly rate.

• Maintenance (PdM_maint.csv): If a component of a machine is replaced, that is captured as a record in this table. Components are replaced under two situations: 1. During the regular scheduled visit, the technician replaced it (Proactive Maintenance) 2. A component breaks down and then the technician does an unscheduled maintenance to replace the component (Reactive Maintenance). This is considered as a failure and corresponding data is captured under Failures. Maintenance data has both 2014 and 2015 records. This data is rounded to the closest hour since the telemetry data is collected at an hourly rate.

• Failures (PdM_failures.csv): Each record represents replacement of a component due to failure. This data is a subset of Maintenance data. This data is rounded to the closest hour since the telemetry data is collected at an hourly rate.

• Metadata of Machines (PdM_Machines.csv): Model type & age of the Machines.

Acknowledgements

This dataset was available as a part of Azure AI Notebooks for Predictive Maintenance. But as of 15th Oct, 2020 the notebook (link) is no longer available. However, the data can still be downloaded using the following URLs:

https://azuremlsampleexperiments.blob.core.windows.net/datasets/PdM_telemetry.csv https://azuremlsampleexperiments.blob.core.windows.net/datasets/PdM_errors.csv https://azuremlsampleexperiments.blob.core.windows.net/datasets/PdM_maint.csv https://azuremlsampleexperiments.blob.core.windows.net/datasets/PdM_failures.csv https://azuremlsampleexperiments.blob.core.windows.net/datasets/PdM_machines.csv

Inspiration

Try to use this data to build Machine Learning models related to Predictive Maintenance.

As a leading data collection and annotation company, we specialize in providing diverse datasets, including images, videos, texts, and speech, to empower machine learning models.

The global Preventive Maintenance Software System market is experiencing steady growth, projected to reach $908.2 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 4% from 2025 to 2033. This growth is fueled by several key factors. Increasing industrial automation and the adoption of Industry 4.0 principles are driving the demand for efficient maintenance strategies. Businesses across diverse sectors, including BFSI (Banking, Financial Services, and Insurance), healthcare, manufacturing, and logistics, recognize the significant return on investment offered by preventive maintenance software in reducing downtime, optimizing operational efficiency, and extending the lifespan of assets. The shift towards cloud-based solutions is further accelerating market expansion, providing scalability, accessibility, and cost-effectiveness compared to on-premises deployments. Furthermore, the growing emphasis on data analytics and predictive maintenance capabilities within these software systems is enabling businesses to move beyond reactive maintenance strategies and adopt proactive approaches, leading to significant cost savings and improved operational reliability. The market is segmented by application (BFSI, Hospitals, Factories, Logistics, Others) and deployment type (On-premises, Cloud-based). While the cloud-based segment is experiencing faster growth due to its inherent advantages, on-premises solutions remain prevalent in sectors with stringent data security and regulatory requirements. Geographically, North America and Europe currently hold significant market shares, driven by high technological adoption and established industrial infrastructure. However, rapidly developing economies in Asia-Pacific, particularly China and India, are expected to contribute significantly to market expansion in the coming years, driven by increasing industrialization and investments in smart manufacturing initiatives. Competitive landscape analysis reveals a diverse range of established players and emerging technology providers, indicating a dynamic market characterized by continuous innovation and competition.