Predictive Maintenance Solutions

Predictive Maintenance Solutions

AI-based solution for Predictive Asset Maintenance and Machine Health Monitoring

Nanoprecise Sci Corp is a predictive maintenance solutions provider that uses cutting-edge AI technology to provide real-time, predictive information about the health and performance of your industrial assets. We offer actionable insights that significantly enhance the service life of machines & equipments, while improving the overall productivity of your operations.

What is Predictive maintenance?

Predictive maintenance optimizes equipment performance by combining IoT sensors, artificial intelligence, and data science. It involves using cloud-enabled technology to monitor and predict maintenance needs based on asset conditions and discover anomalies that have the potential to cause unexpected failures. As a result, manufacturers benefit greatly from anticipating equipment maintenance needs to reduce costs and maximize uptime.

The fundamental principle utilized to monitor industrial assets in real-time is Condition Based Monitoring. Advanced IIoT sensors are used to capture complex machine health data such as vibration, acoustics, temperature and RPM among others. The acquired data is then analyzed to find any deviations in asset performance or abnormalities that would be impossible to notice with conventional .equipment.

Predictive maintenance does not end with the detection of performance concerns. It identifies the underlying causes of poor asset performance using predictive analytics. Predictive Maintenance Solutions can also predict when a monitored equipment will fail if corrective maintenance procedures are not implemented.

Why Do You Need Predictive Maintenance Solutions?

Asset Security and Dependability

Unreliable assets cause unplanned downtime and process disruption. Real-time asset health information from the site helps reliability teams to schedule maintenance events with the slightest disturbance to plant operations.

Improve Production and Yield

Predictive Maintenance Solutions increase operational efficiency by making sure assets are operating at their optimum capacity. This maximizes plant uptime and yield. In addition, a data-driven approach reduces scenarios that could severely affect plant output or production.

Obtain Cost-Efficiency

Predictive maintenance reduces plant downtime by utilizing real-time data and offer root cause analysis & maintenance recommendations to aid decision-making. This reduces overspending and additional inventory costs, thereby lowering maintenance costs.

Nanoprecise solutions provide advanced early alerts, so you can take proactive measures to optimize machine uptime and avoid costly breakdowns. In addition, our AI-based technology aids in predictive and prescriptive maintenance, ensuring that your equipment is always running at peak performance.

Monitoring Methods for Predictive maintenance

Sensors are commonly used to measure and monitor systems and equipment. Sensors capture various types of data and send it to a computer, which displays it in an understandable format. For example, the sensors may collect temperature, acoustic emission, vibration, humidity, RPM, & magnetic flux data and securely transmit them to the cloud platform for complete analysis.

Predictive maintenance is based on internet of things (IoT) sensors that are wirelessly linked to a cloud-based platform that collects and analyses machine data. 

With the help of MachineDoctor a completely wireless, battery-powered edge sensor, data can be sent to the cloud for analysis via cellular network. Additionally, advanced edge analytics enable continuous monitoring without constant connectivity.  The AI-powered predictive maintenance solution from Nanoprecise provides real-time monitoring of equipment condition and predicts when maintenance is needed before a costly breakdown occurs. This proactive approach helps clients reduce downtime, increase production, and improve the safety of their equipment.

Use of AI and Machine Learning

AI-Based predictive maintenance can be applied to many use cases in manufacturing businesses. It uses advanced machine learning algorithms to analyze massive volumes of data generated during production and offers critical insights to achieve manufacturing excellence.

Machine learning algorithms use vast volumes of historical data to run numerous scenarios and forecast what will go wrong and when. Advanced artificial intelligence algorithms understand a machine’s regular data behavior and use it as a baseline to detect and alert to deviations in real-time.

Predictive Maintenance Applications

Nanoprecise technology is versatile and can be applied to various industries, including manufacturing, transportation, power generation, mining and oil and gas. Our team of experts works closely with our clients to understand their specific needs and tailor our solutions to meet those needs.

Customised to your Industry Needs


Reducing Downtime in Metal Industry

Our IoT driven Predictive maintenance solution helps to reduce downtime, monitor, collect exchange and analyze data from machines to enhance manufacturing processes of the metal industry.

Scaling Iot in Manufacturing Industry

Machine failure in the mines? No worries.

Our solutions can add immense value to your entire mining supply chain by harnessing the power of Industry 4.0. The asset performance will be optimized, costs and machine downtime can be reduced leading to a boost in ROI.

Predictive Maintenance in Mining Sector

No more unplanned downtime in Cement Industry

Our Industry 4.0 digital solutions can help you tackle the challenges in cement production such as large energy consumption, high costs and complex processes.

Our advanced analytics can simplify complexities and help in real-time decisionmaking. Equipment lifespan and reliability will amplify due to our condition monitoring system.

Predictive Maintenance in Cement Plant

Protect your assets with Zone Approved Solution

Our digitization solutions in industrial equipment maintenance can help oil and gas companies streamline maintenance. Our predictive analytics and conditional data monitoring help anticipate failures, reducing unplanned maintenance and unscheduled downtime.

Predictive Maintenance in Oil & Gas Plant

No more Downtime, Keep your Machines Running in Chemical Plants

Our AI driven analytics can propel your chemical business to new heights of reliability by optimizing asset longevity and impacting top-line growth through proactive identification of upcoming machine failures. IoT driven asset maintenance solutions can provide immense flexibility and agility to production.

Predictive Maintenance in Chemical Plant

Request a Call Back

    Contact Us

    Fill in the details and our team will contact you back

    Marketing Permissions

    The information you provide on this form will only be used to provide you with updates and personalized marketing. Your privacy is important to us! Please let us know how you would like to keep in touch:

    We will send you occasional emails about promotions, new products and important updates to keep you in the loop.

    By clicking below to submit this form, you acknowledge that the information you provide will be processed in accordance with our Privacy Policy.

    Frequently Asked Questions

    Cost savings with predictive maintenance solutions can be incredibly high. On average, there are cost savings of 18% to 25% in maintenance expenses, with additional savings and benefits from greater uptime.

    Predictive maintenance solutions send alerts & notifications via email and SMS to maintenance & reliability professionals, in order for them to take corrective actions and prevent unplanned downtime.

    Predictive maintenance solutions can provide considerable financial gains with a significant ROI, a 25%-30% reduction in maintenance expenses, a 70%-75% reduction in breakdowns, and a 35%-45% decrease in downtime.

    Wireless predictive maintenance has numerous benefits, including easy implementation with no additional IT infrastructure, faster installation,, total operational visibility, increased productivity, and improved worker safety.

    Solution providers protect IoT networks against cyber threats by ensuring that the system complies to international cybersecurity standards. In addition, advanced systems use edge computing that allows for full time monitoring and analysis with limited access to internet, thereby reducing the chances of getting hacked. Additionally, IoT sensors are connected to the internet via a reliable cellular networks that offer improved coverage and better security.