Predictive Maintenance with IoT

Predictive maintenance with IoT enables strategic allocation of resources to optimize equipment performance throughout the organization. It allows to avoid unwanted pitfalls that causes wastage of time and resources while delivering productive results from the start.

Talk to Our Expert

Introduction

Across the world, manufacturing equipment faces failures and downtime, while in different environments, due to the nature of repetitive tasks being performed by them. However, with a higher demand for efficiency and quality in production & manufacturing, these unplanned downtime causes delays and customer-loss, thereby hitting the bottom-line of the organization. Therefore, it is essential to limit the possibility of unplanned downtime as much as possible, to improve the bottom-line and gain competitive advantage.

predictive_maintenance_iot_2

What is Predictive Maintenance with IoT?

predictive_maintenance_iot_3

Industrial Internet of Things (IoT), also known as IIoT, is the use of the Internet of Things (IoT) in the manufacturing/industrial sector. It constitutes the use of Wireless Industrial IoT sensors & applications, to connect machines and equipment sets, in order to facilitate machine-to-machine communication that improves the efficiency of overall manufacturing processes.

Predictive maintenance with IoT refers to the use of a data-driven approach, that analyses the equipment condition to predict when that equipment requires maintenance. It is a technique that can significantly improve the performance and lifetime of assets.

Predictive maintenance is a multi-step process that helps maintenance & reliability professionals monitor the equipment health to prevent failures and unplanned downtime. It uses data from sensors and predictive algorithms to estimate the correct time of equipment failure and schedule maintenance activities accordingly. It identifies the root cause of issues in complex machinery and the parts that need replacement. Predictive maintenance process generally involves:

  • Remote Condition Monitoring of equipment to collect real-time performance data.
  • Gaining actionable insights from the analysis of complex machine condition data.
  • Taking remedial measures based on the insights obtained, to maximize the asset uptime.

Technologies that drive Predictive Maintenance


At a macro level, Predictive Maintenance with IoT uses advanced technologies such as Smart Sensors, Internet of Things, Big Data, Machine Learning, Cloud Computing, Edge Computing and Wireless Communication Networks. Engineers and Professionals bring these technologies together to build a robust Predictive Maintenance Solution. Predictive maintenance technology is based on a simple architecture as mentioned below:

Wireless Industrial IoT Sensors are installed in the proximity of an asset / machine, which then captures various output parameters (such as vibration, temperature, sound etc.) of these machines, and converts it into signals.
These signals are transmitted via wireless networks to the servers located either on cloud or on-premise. Generally, these signals are transmitted to the server using gateways. However, newer advancements have paved the way for smart sensors that can transmit signals without the need for a gateway. These sensors work on Cellular or LoRa networks, thereby eliminating the hassle of complex wiring and multiple components.
Once the signals are received by the server, machine learning algorithms filter & analyse it to provide meaningful insights about the health and performance of the machines.

Benefits of adopting Predictive Maintenance with IoT

Predictive Maintenance has the potential to reduce unplanned downtime and prevent asset failures. It facilitates remote condition monitoring of critical industrial assets and ensures proactive asset maintenance. The goal of predictive maintenance with IoT is to improve the health & performance of machines, leading to reduced downtime, increased production and improved workplace safety.

Predictive Maintenance with IoT offers myriad benefits to the manufacturing operators, that help them gain significant competitive advantage. These benefits include:

  • Complete visibility of manufacturing and production operations
  • Increased Operational Efficiency
  • Reduced unplanned downtime
  • Smooth running of production facilities at lower costs.
predictive_maintenance_iot_4

Predictive Maintenance system analyses historical as well as real-time performance data of the machines using predictive algorithms to detect faults before it occurs and prevent subsequent failures. It also allows to maximize asset uptime and optimize maintenance costs & resources.

Important Steps for implementing Predictive Maintenance with IoT

predictive_maintenance_iot_5
Following are some important steps for implementing a robust Predictive Maintenance solution:
  • Establishing a clear plan to start small implementation programs
  • Choosing the right assets
  • Choosing the right condition monitoring methods to monitor patterns in real-time
  • Establishing a holistic Data Collection & Analysis mechanism
  • Pilot Testing of the solution 
  • Deciding on an appropriate response procedure
  • Building a clear data analysis strategy
  • Establishing a continuous improvement process for implementation at scale.

These practices should assist maintenance & reliability professionals to deal with issues and extract value from full scale implementation of predictive maintenance.

Conclusion

Industrial IoT Predictive Maintenance has the potential to offer significant competitive advantage to organizations, which is an essential component for continued success in an ever-changing business environment. Ultimately, Predictive Maintenance with Industrial IoT opens a brave new world for manufacturers aiming to reduce downtime, increase productivity, improve worker safety and lower costs.

Nanoprecise is an Industrial IoT Predictive Maintenance solution provider that offers real-time predictive information about the genuine health and performance of industrial assets. Nanoprecise offers IoT Solutions for Industrial Manufacturing with our unique 6-in-1 Wireless Industrial IoT Sensor and patented AI-based analytics platform. The Industrial IoT Predictive Maintenance Solutions from Nanoprecise uses a combination of AI + IoT + LTE-driven seamless monitoring, to offer prescriptive diagnostics. Nanoprecise specializes in scaling Industrial IoT across various sectors to empower maintenance and reliability professionals with the right data at the right time.

Customised to your Industry Needs

Industries

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

    Predictive Maintenance with IoT aims to predict the failures in machines before they occur, so as to maximize the machine uptime and minimize the maintenance frequency.

    Industrial IoT enables machine-to-machine communication to provide real-time information about their performance. It captures complex machine data, to provide meaningful insights including early fault detection and failure prediction.

    Predictive Maintenance with Industrial IoT Solution analyzes the complex machine data and translates it into meaningful insights, thereby empowering maintenance and reliability professionals to take faster & accurate actions and prevent unplanned downtime.

    An automated Predictive Maintenance Solution consists of a combination of IoT Hardware and analytics Software. The hardware captures different machine output parameters and transmits it in the form of signals to the analytics software. The software analyses it to detect faults before it happens, thereby preventing unplanned downtime and increasing the operational efficiency of the machines.

    Industrial IoT enables manufacturing organizations to automate the data collection and analysis process. This helps maintenance and reliability professionals to focus on the maintenance activities rather than on data collection & analysis. It also reduces the maintenance costs and optimizes resources, by offering increased transparency to the overall manufacturing operations.