Predictive Maintenance System
Intelligent IoT Data Sensor Analytics
Datahoist Can Turn Your Sensor Data into Analytics
Datahoist’s Predictive Maintenance capabilities stem from our patent-pending, predictive maintenance analytics algorithms. Our software can monitor multiple operational sensors simultaneously and provide our users with critical insight into their elevator, escalator or other machinery’s operation and performance. We’ve designed it to work with most elevators, escalators and other industrial devices.
IoT Sensor Data Makes Predictive Analytics Possible
The Datahoist Predictive Analytics System is at the heart of Prescriptive Maintenance Solution. It provides a user-tailored, innovative solution that continuously learns and adapts as it monitors the health of your devices. We capture and manage sensor and other operational data using Microsoft’s Azure IoT Cloud or any other private or public cloud IoT service. With our patent-pending Predictive Analytics system, we provide a user tailored, comprehensive health indicator for each device, which can be customized and configured according to your needs.
What is IoT Analytics?
IoT analytics is the process of collecting, processing, and analyzing data generated by Internet of Things (IoT) devices and sensors to extract meaningful insights and drive business decisions. It involves using various data science techniques to turn raw data into actionable information, enabling businesses to optimize operations, improve products, and enhance customer experiences.
Easily Climb the Maintenance Pyramid
Datahoist’s products, including our IoT Analytics System makes it possible for you to climb the maintenance pyramid by helping you develop a more prescriptive maintenance process that leads to greater customer satisfaction, lower downtime and safer operations.
Maintenance Pryamid
Prescriptive Maintenance
Integrated Solution with early failure predictions & recommended service. [Click to Learn More…]
Predictive Maintenance
Sensors and advanced algorithms predict future failure. [Click to Learn More…]
Proactive Maintenance
Service performed when sensors show signs of decreasing performance.
Preventive Maintenance
Service equipment on a periodic basis to prevent failures. [Click to Learn More…]
Reactive Maintenance
Service performed only after equipment has failed.
How It Works
Datahoist’s AI-driven Prescriptive Maintenance Solution analyzes operational data, allowing you to identify subtle changes in device performance and operational conditions. By capturing, identifying, and classifying these operational signatures, we can predict maintenance needs and help improve overall performance.
Focus on Elevators & Escalators
The current focus of our product offering is on elevators, escalators, and similar devices, but our technology can be easily trained for other in-motion devices. Seamlessly integrating with a wide range of devices, systems, and platforms, our Advanced Data Intelligence & Analysis system helps you schedule maintenance and repairs proactively, ensuring maximum uptime.
KPIs Tracked for Elevators
- Door time consistency
- Flight time consistency
- Releveling
- Signal Analysis/Fault recognition
- Motion Analysis
- Out of service states
- and more…
Approachable & Useful Dashboards
Using our predictive analytics, your users are provided access to a comprehensive dashboard providing an “at-a-glance” health check of all equipment. Our dashboards are tailored for Elevators, escalators & other people movers, but can be customized for any industrial device that monitors vibration, acceleration, temperature, or other sensor data.
Features:
- Quickly highlights problem devices
- Indicates devices that are operating out of normal conditions base on our predictive continuous analysis
- Details issues out of norm for fast determination of potential issues.
- Our dashboards can be tailored to your specific needs and the data needs of your devices.
Customizable Status Indicators
Our customizable status indicators are controlled by identifying subtle changes in the device operation signature of the sensor set. We identify differences with known operating conditions and correlate them to device performance & operational condition. By analyzing these unique operational signatures, we identify the conditions that usually indicates a change in performance in the monitored equipment and could forewarn of a need for service (either immediate or near-term).
Predictive Analytics
Our predictive quality diagnosis is tailored for your specific device type. It helps ensure your devices, like elevators & escalators, are in service longer without unforeseen breakdowns or inconveniences.
We track all the critical information for your devices, including:
- Location
- Make, Model & other service info
- Service history
- Sensor history
- Other customer specific info
Capturing, identifying and classifying these operational signatures for elevators and escalators forms the basis of our AI driven Prescriptive Maintenance Solution. Our future plans include training a comprehensive AI tool for Perscriptive Maintenence for a variety of in-motion devices. Our work in elevators and escalators is the initial market segments for us to take this technology.
Easy Data Access
Users can view near real-time updates or set-up daily email reports.
Additionally, through Microsoft Power BI, customers can opt to view increased business analytics.
Frequently Asked Questions (FAQs)
What is Prescriptive Maintenance?
Prescriptive maintenance is a maintenance strategy that leverages data, analytics, and machine learning to not only predict when equipment might fail (like predictive maintenance) but also to recommend specific actions to prevent those failures and optimize performance. It moves beyond prediction to provide actionable insights for maintenance teams.
What is Preventive Maintenance?
Preventive Maintenance relies on a predefined maintenance schedule to avoid potential equipment failures. This outdated maintenance strategy relies on the expected condition of the equipment based on a historical baseline of performance and failure rates. It often leads to unnecessary or premature equipment and component replacement driving up costs and reducing the availability of maintenance technicians.
What is Predictive Maintenance?
Predictive maintenance builds on condition-based monitoring to optimize the performance and lifespan of equipment by continually assessing its health in real time.
By collecting data from sensors and applying advanced analytical tools and processes such as machine learning, predictive maintenance can identify, detect, and address issues as they occur, as well as predict the potential future state of equipment, and so reduce the risk of failure.
Predictive maintenance enables your business to anticipate equipment failures and schedule maintenance when and where it’s immediately needed. It arms you with the information needed to run your assets at peak performance without pushing them too far and risking costly breakdowns. By connecting IoT-enabled enterprise assets, applying advanced analytics to the real-time data they generate, and using the associated insights to inform cost-effective, efficient maintenance protocols, predictive maintenance prevents equipment failure and downtime, and extends the overall lifetime of vital assets.
What is the difference between Preventive and Predictive Maintenance?
- Preventive maintenance utilizes past performance and the knowledge and experience of engineers and operators. It includes routine, periodic, planned, or time-based maintenance. While it often prevents breakdowns, it can be inexact, which may lead to expensive maintenance before it’s needed or to unnoticed weaknesses in the maintenance process. Preventive maintenance happens at times that are pre-set, often long in advance.
- Predictive maintenance is made possible when IoT networks integrate all enterprise assets into a live ecosystem. The ability to transmit and analyze data in real time, means that live asset condition monitoring – rather than calendars – becomes the foundation for maintenance protocols. Predictive maintenance happens in real time, exactly when and where it’s needed.
Predictive maintenance also differs from preventive maintenance in the diversity and breadth of real-time data that is used in monitoring the equipment. Various condition monitoring techniques such as sound (ultrasonic acoustics), temperature (thermal), lubrication (oil, fluids) and vibration analysis can identify anomalies and provide advance warnings of potential problems. A rising temperature in a component, for example, might indicate airflow blockages or wear and tear. Unusual vibrations might indicate misalignment of moving parts. Changes in sound can provide early warnings of defects that can’t be picked up by the human ear.
Predictive maintenance enables your business to anticipate equipment failures and schedule maintenance when and where it’s immediately needed. It arms you with the information needed to run your assets at peak performance without pushing them too far and risking costly breakdowns. By connecting IoT-enabled enterprise assets, applying advanced analytics to the real-time data they generate, and using the associated insights to inform cost-effective, efficient maintenance protocols, predictive maintenance prevents equipment failure and downtime, and extends the overall lifetime of vital assets.
How does Predictive Maintenance work?
Predictive maintenance relies on various technologies including the Internet of Things (IoT), predictive analytics, and artificial intelligence (AI). Connected sensors gather data from assets such as machinery and equipment. This is collected at the edge or in the cloud in an AI-enabled enterprise asset management (EAM) or computerized maintenance management system (CMMS). AI and machine learning are used to analyze the data in real time to build a picture of the current condition of the equipment. Thereafter, triggering an alert if any potential defect is identified and delivering it to the maintenance team.
As well as providing defect warnings, advances in machine learning algorithms enable predictive maintenance solutions to make predictions about the future condition of equipment. These can be used to drive greater efficiency in maintenance-related workflows and processes such as just-in-time work order scheduling and labor and parts supply chains.
Furthermore, the more data collected the more insights are generated and the better the predictions become. This gives businesses the confidence that equipment is working optimally.
What are the benefits of Predictive Maintenance?
Benefits from a predictive maintenance strategy center around anticipating equipment faults and failures, reducing maintenance and operating costs by optimizing time and resources, and improving the performance and reliability of equipment. The implementation of predictive maintenance systems has led to impressive results across multiple industries. A 2022 report from Deloitte cites numerous quantifiable improvements. These include up to a 15% reduction in downtime, a 20% increase in labor productivity, and a 30% reduction in inventory levels with less need to stock just-in-case parts.
Optimizing asset performance and uptime can reduce costs. Advance warning of potential faults results in fewer breakdowns as well as reduced planned maintenance or unplanned downtime. Greater continuous condition visibility enhances the lifetime reliability and durability of equipment. The use of AI can more accurately forecast future operations. This latter benefit is paramount in a world where rising prices and unpredictable events like the pandemic and climate-related natural disasters exposed the need for more predictable spare parts inventory and labor costs and a lower environmental impact from operations.
Productivity can be increased by reducing inefficient maintenance operations. Enabling a faster response to problems via intelligent workflows and automation, and equipping technicians, data scientists and employees across the value chain with better data with which to make decisions. The upshot is improved metrics such as mean time between failures (MTBF) and mean time to repair (MTTR), safer working conditions for employees, and revenue and profitability gains.