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.

Dataset

This dataset was created by Wasurat Soontronchai

Contents

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The Industrial AI Software market is booming, projected to reach $114.68M in 2025 with a 35.97% CAGR. Discover key drivers, trends, and regional insights in this comprehensive market analysis, featuring top players like IBM, Microsoft, and Google. Explore the potential of AI in automotive, healthcare, and more. Recent developments include: April 2023: Siemens and Microsoft announced their collaboration to leverage the capabilities of generative artificial intelligence (AI) in enhancing innovation and efficiency across all stages of product development, from design and engineering to manufacturing and operation. This partnership involves the integration of Siemens' Teamcenter software for product lifecycle management (PLM) with Microsoft's collaborative platform Teams, as well as utilizing Azure OpenAI Service's language models and other Azure AI capabilities., February 2023: Mercedes-Benz revealed its strategic initiative to digitize the Vehicle Product Lifecycle by partnering with NVIDIA AI and Omnivers, a software platform designed for creating and operating metaverse applications. This digital transformation enables Mercedes-Benz to establish a virtual workflow, empowering them to swiftly respond to supply chain disruptions and adapt assembly line configurations as necessary.. Key drivers for this market are: Increase in Usage of Big Data Technology in Manufacturing, Expanding application base and growing emphasis on adoption of digital transformation practices to realize cost savings. Potential restraints include: Need for Skilled Workforce. Notable trends are: Retail and Consumer Packaged Goods is Expected to Hold Significant Share of the Market.

Azure IoT Edge Modules Market Outlook

As per our latest research, the Azure IoT Edge Modules market size reached USD 1.54 billion in 2024, reflecting the rapid adoption of intelligent edge computing solutions across industries worldwide. The market is experiencing robust expansion, recording a CAGR of 17.6% during the forecast period. By 2033, the global Azure IoT Edge Modules market is projected to attain a value of USD 7.45 billion. The primary growth factor driving this market is the increasing demand for real-time data processing and analytics at the edge, enabling enterprises to optimize operations, reduce latency, and enhance decision-making capabilities in mission-critical environments.

One of the core drivers behind the surging growth of the Azure IoT Edge Modules market is the proliferation of connected devices and the exponential rise in data generated at the edge. Organizations are increasingly recognizing the limitations of traditional cloud-centric architectures, particularly in scenarios where low latency, bandwidth optimization, and immediate data processing are paramount. Azure IoT Edge Modules empower enterprises to process data locally, ensuring faster insights and more agile responses to operational events. This paradigm shift is particularly vital in sectors such as manufacturing, energy, and transportation, where milliseconds can make a significant difference in safety, efficiency, and profitability. As digital transformation initiatives intensify, the market is witnessing heightened investments in edge computing frameworks, with Azure IoT Edge Modules emerging as a preferred solution owing to their seamless integration, scalability, and robust security features.

Another significant growth factor is the increasing adoption of artificial intelligence (AI) and machine learning (ML) at the edge. Azure IoT Edge Modules are uniquely positioned to facilitate the deployment of AI models directly on edge devices, enabling predictive maintenance, anomaly detection, and intelligent automation without the need for constant connectivity to the cloud. This capability is revolutionizing industries by enabling smarter, self-sufficient devices and systems that can operate even in remote or intermittently connected environments. The ability to run advanced analytics and AI workloads at the edge is driving innovation in applications ranging from smart cities and healthcare to energy management and logistics. Furthermore, the flexibility and modularity of Azure IoT Edge Modules allow organizations to customize their edge deployments according to specific use cases, further propelling market growth.

The growing emphasis on data privacy, regulatory compliance, and cybersecurity is also catalyzing the adoption of Azure IoT Edge Modules. As organizations grapple with stringent regulations such as GDPR and sector-specific mandates, processing sensitive data locally at the edge provides a strategic advantage by minimizing exposure and ensuring compliance. Azure IoT Edge Modules offer comprehensive security features, including encrypted communications, secure device provisioning, and robust access controls, which are essential for safeguarding critical infrastructure. The market is also benefiting from Microsoft's commitment to continuous innovation, ecosystem partnerships, and support for open-source frameworks, which collectively foster a vibrant developer and integrator community. This collaborative approach is accelerating the deployment of edge solutions across diverse verticals.

From a regional perspective, North America continues to dominate the Azure IoT Edge Modules market, driven by early technology adoption, substantial investments in digital infrastructure, and a strong ecosystem of technology providers and end-users. However, the Asia Pacific region is witnessing the fastest growth, fueled by large-scale smart city initiatives, rapid industrialization, and increasing demand for automation across manufacturing and logistics sectors. Europe is also a significant market, characterized by a strong focus on Industry 4.0, sustainability, and regulatory compliance. Latin America and the Middle East & Africa are emerging as promising markets, supported by growing investments in digitalization and infrastructure modernization. The regional dynamics are shaped by varying levels of technological maturity, regulatory environments, and sector-specific adoption patterns, which collectively influence the trajectory of the Azure IoT Edge Modules market worldwide.
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The market for mission-critical IoT platforms is experiencing robust growth, driven by increasing demand for reliable and secure connected devices across diverse sectors like healthcare, manufacturing, and transportation. The convergence of technologies such as 5G, edge computing, and AI is fueling innovation and expanding the capabilities of these platforms. While precise figures for market size and CAGR are unavailable, a reasonable estimation, based on industry reports and the presence of major players like PTC, Cisco, and Microsoft, suggests a market value exceeding $15 billion in 2025, with a compound annual growth rate (CAGR) of 15-20% projected through 2033. This growth is fueled by the rising adoption of IoT in mission-critical applications requiring high availability, real-time data processing, and stringent security measures. Key drivers include the need for improved operational efficiency, predictive maintenance, remote monitoring, and enhanced safety across various industries. However, challenges such as data security concerns, integration complexities, and the need for robust regulatory frameworks pose restraints on market expansion. The competitive landscape is characterized by a mix of established technology giants and specialized IoT solution providers. Companies like PTC (ThingWorx), Cisco (Jasper), Microsoft, and IBM are leveraging their existing infrastructure and expertise to capture market share. Meanwhile, smaller, more agile companies are focusing on niche applications and innovative technologies. The market is segmented based on deployment (cloud, on-premise, hybrid), vertical industry (healthcare, manufacturing, energy, etc.), and connectivity technology (cellular, Wi-Fi, etc.). Regional variations in adoption rates exist, with North America and Europe currently leading the market, followed by Asia-Pacific which is expected to exhibit significant growth in the coming years. This growth trajectory is influenced by increasing government initiatives to promote digital transformation and the expansion of 5G networks in these regions. The forecast period of 2025-2033 presents significant opportunities for businesses to capitalize on the evolving needs of mission-critical applications.

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Deep learning has proven to show superior performance in certain domains such as object recognition and image classification. It has also gained popularity in domains such as finance where time-series data plays an important role. Predictive Maintenance is also a domain where data is collected over time to monitor the state of an asset with the goal of finding patterns to predict failures which can also benefit from certain deep learning algorithms. Among the deep learning methods, Long Short Term Memory (LSTM) networks are especially appealing to the predictive maintenance domain due to the fact that they are very good at learning from sequences. This fact lends itself to their applications using time series data by making it possible to look back for longer periods of time to detect failure patterns. In this notebook, we build an LSTM network for the data set and scenerio described at Predictive Maintenance Template to predict remaining useful life of aircraft engines. In summary, the template uses simulated aircraft sensor values to predict when an aircraft engine will fail in the future so that maintenance can be planned in advance.

Describing the dataset

The dataset consists of sensor readings from a fleet of simulated aircraft gas turbine engines operating conditions as a multiple multivariate time series. The dataset consists of separate training and test sets. The testset is similar to the training set, except that each engine’s measurements are truncated some (unknown) amount of time before it fails. The data is provided as a ZIP-compressed text file with 26 columns of numbers. Each row represents a snapshot of data taken during a single operational cycle and each column represents a different variable. Datasets taken from Microsoft Azure.

### Context

This dataset was taken from the example https://gallery.azure.ai/Experiment/Predictive-Maintenance-Modelling-Guide-Experiment-1 After data engineering creating cycle columns based on 24h the goal is to predict the RUL of each machine.

### Content

The dataset has telemetry reading and error identification, maintenance, and failure:

The telemetry was modified to 24h, with the original data being read out every hour. Volt, telemetry, pressure, and vibration are measured. The errors are identified by an error or not, and this error can generate maintenance that can become a failure identification.

Column description machineID - Machine ID, reads from 100 machines datetime - Date of telemetry time_in_cycles - Cycle reading voltmean_24h - Volt telemetry average of the last 24h rotatemean_24h - Rotate telemetry average of the last 24h pressuremean_24h - Pressure telemetry average of the last 24h vibrationmean_24h - Vibration telemetry average of the last 24h voltsd_24h - Volt telemetry standard deviation of the last 24h rotatesd_24h - Rotate telemetry standard deviation of the last 24h pressuresd_24h - Pressure telemetry standard deviation of the last 24h vibrationsd_24h - Vibration telemetry standard deviation of the last 24h voltmean_5d - Volt telemetry average of the last 5 days rotatemean_5d - Rotate telemetry average of the last 5 days pressuremean_5d - Pressure telemetry average of the last 5 days vibrationmean_5d - Vibration telemetry average of the last 5 days voltsd_5d - Volt telemetry standard deviation of the last 5 days rotatesd_5d - Rotate telemetry standard deviation of the last 5 days pressuresd_5d - Pressure telemetry standard deviation of the last 5 days vibrationsd_5d - Vibration telemetry standard deviation of the last 5 days error1 - Error identification 1 error2 - Error identification 2 error3 - Error identification 3 error4 - Error identification 4 error5 - Error identification 5 comp1 - Component 1 maintenance cycle comp2 - Component 2 maintenance cycle comp3 - Component 3 maintenance cycle comp4 - Component 4 maintenance cycle model - Machine model age - Machine age RUWeek - Time in weeks for failure failure - Component identification that failed failed - Failed or not RUL - Remaining Useful Life in cycles by failure and machine RUL_I - Inverse RUL

### Inspiration I would like to predict the remaining useful life of each machine (RUL)

### Non-engineering data source https://gallery.azure.ai/Experiment/Predictive-Maintenance-Modelling-Guide-Experiment-1

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