Condition Monitoring

Optimizing machine health with Condition Monitoring

Optimizing machine health with Condition Monitoring


Industry 4.0 aims to bring operational excellence by introducing the Industrial Internet of Things (IIoT) to the industries and factories. It allows machine health monitoring using IIoT, streamlining the process and increasing efficiency. In this blog, we’ll share why IIoT machine monitoring is useful and how it can help you?


Importance of Machine Health Monitoring in the Industrial 4.0 Era

Despite applying the best reactive and preventative maintenance strategies, industries lose a lot of money & time because of unplanned downtime, machine failures, and wasted maintenance cycles. Unplanned downtime decreases plant productivity and hinders the supply chain. To overcome this, plants need to adopt Condition Monitoring technologies.

IIoT machine monitoring offers real-time insights to assist maintenance teams in making better decisions, enhancing the machine’s efficiency and extending its lifetime. IIoT plays a vital role in enabling plant reliability by:

  1. Providing robust connectivity across the plant
  2. Catering to the growing shortage of plant workers.
  3. Helping in planning and scheduling maintenance strategies.
  4. Bringing more profit against its initial implementation cost.

It also creates a safer working environment for plant workers by reducing the chances of machine failures. 


Benefits of Machine Health Monitoring using IIoT

Improvement in the overall efficiency of manufacturing

IIoT machine monitoring machinery considerably increases the overall plant efficiency. It increases the cost efficiency by cutting unnecessary maintenance and decreasing unplanned downtime. Unplanned downtime constitutes a 40-50% loss in efficiency. Condition monitoring predicts the impending failures and helps in curing them beforehand.

Real-time monitoring and required maintenance of all the plant assets enhance the plant’s productivity and sustain it. It also extends the plant equipment’s lifetime, saving many costs that otherwise would go in vain.

Considerable Reduction in Waste

IIoT machine monitoring can help industries in waste management. Defective items are the most significant manufacturing waste from plants. Trivial machine malfunctions often get ignored, which causes the production of defective items or sub-par output quality. It costs money, resources, and man-hours. Also, starting up after unplanned downtimes produces unprocessed/semi-processed goods, further increasing the overall plant waste. IIoT machine monitoring can eliminate this waste by foretelling the possible threats.

Intelligent adoption of IIoT-enabled solutions also reduces the burden of excessive maintenance, lubrication, and spare parts waste. Assessing and predicting machine failures saves time and resources, which would otherwise go to waste.

Improved communication & decision making

Machine health monitoring using IIoT improves communication by providing 360-degree visibility of manufacturing operations to all the right people. Advanced solutions connect plant equipment to a manager, manager to the operator, and operator to operator effectively, reducing the chances of delayed communication.

Providing the right & timely information across the plant boosts the plant productivity multiple folds. Usually, a lot of time gets wasted in planning and scheduling maintenance strategies. IoT-based machine health monitoring systems capture the fault and track down the root cause to advise you on the best approach to tackle it.

Real-time Data Collection, Analysis, and Alerts

IIoT-based condition monitoring systems collect real-time data from all the machinery and analyse and assess them according to the recommended performance levels. If the machine health fails to meet the set parameters, the platform immediately alerts the maintenance manager and conveys the problems with the recommendations to take care of it.

The sensors record data from various equipment to perform vibration analysis, oil analysis, temperature analysis, and other relevant analyses. After analyzing, if it detects any issue, it quickly notifies the possible reasons. For example, the reports may suggest engine erosion if higher than usual iron content is found in the oil analysis. On the other hand, if a higher range of a combination of iron, Aluminium, and chrome is found, it may signal the upper cylinder wear. The maintenance manager can then take immediate action on this.


Even the best reactive and preventative maintenance strategies couldn’t do justice to the cost and productivity. So, industrial revolution 4.0 brings advanced machine health monitoring using IIoT technology to address production and maintenance issues. The technology gained its importance by tackling day-to-day problems in various industries. It benefits industries by reducing plant wastes, collecting and analyzing real-time data, streamlining communication, and increasing overall plant reliability.

Machine health monitoring using IIoT is vital in Industry 4.0 because it helps minimize unplanned downtime, reduces maintenance costs, and enhances overall plant efficiency. By providing real-time insights and predictive analytics, it allows for proactive maintenance, thus optimizing operations and increasing profitability.
IIoT-based machine health monitoring improves manufacturing efficiency by reducing unplanned downtime (which can lead to 40-50% efficiency loss), extending equipment life, and optimizing maintenance schedules. It also enhances workplace safety by preemptively addressing potential machine failures.
IIoT machine monitoring minimizes waste by predicting and preventing machine malfunctions that can lead to defective products or sub-par output quality. It optimizes maintenance practices, reduces unnecessary lubrication and spare parts usage, and improves overall resource utilization.
Real-time data collection through IIoT sensors allows for continuous monitoring of machine performance metrics such as vibration, oil quality, and temperature. Analysis of this data helps identify anomalies and potential failures early, triggering alerts for proactive maintenance actions.
IIoT-based machine health monitoring systems facilitate improved communication by providing comprehensive visibility into manufacturing operations. This connectivity ensures timely information flow between operators, managers, and maintenance teams, enhancing operational efficiency and reducing response times.
IIoT-driven systems offer significant advantages over traditional reactive and preventive maintenance approaches by enabling predictive maintenance. They optimize maintenance strategies, reduce costs associated with downtime and repairs, and support data-driven decision-making for better operational outcomes.
Condition Monitoring

How to choose a maintenance solution for your plant in 2022?

How to choose a maintenance solution for your plant in 2022?


Revolutions are synonymous with disruptions. Industry 4.0 is nothing different. It demands new and advanced technologies for manufacturing plant maintenance and discarding obsolete plant maintenance processes at a much faster pace. It sometimes becomes overwhelming to understand and adopt new technologies as a plant head. So, here, we have an article to help you choose the right maintenance solution for your plant in 2022.

In this article, we’ll majorly talk about how you can choose the right Predictive Maintenance solution for your plant. But let’s start with the 3 basic types of industrial plant maintenance solutions available in the market.

Types of industrial plant maintenance

Reactive Maintenance

Maintenance that is out of reaction rather than duty and is performed only after the equipment is finally broken. This obsolete maintenance strategy can save you money in the short term but eventually increases your losses by increasing machine downtime, inefficiency, and frequent failures

Preventative Maintenance

Preventative plant maintenance requires scheduled check-ups routined on industry standards, and it involves timely maintenance and carry-out tasks like a belt and filter changes regularly. This maintenance strategy for plants and equipment increases equipment life but requires regular labor for check-ups and maintenance.

Predictive Maintenance

Predictive plant maintenance leverages artificial intelligence, cloud storage, and IoT to provide real-time data on plant equipment. It diagnoses the real-time condition of in-service equipment, and then the required maintenance schedule is followed. It also reduces the operating cost by 12-18% and provides a safer working environment.

Objectives of a Predictive Plant Maintenance Solution

The objective of opting for a plant maintenance solution is to elongate the life of plant equipment and operate them in an optimum condition at minimum cost. Here are all the significant objectives below-


  1. To maintain the peak productivity of the manufacturing plant.
  2. To obtain the optimum working capacity of equipment at the lowest possible cost.
  3. To minimize the losses from unwanted breakdowns and downtimes.
  4. To provide a safe working environment for plant workers.
  5. To protect the equipment from frequent breakdowns and efficiency loss.
7 most important questions to consider before choosing a Predictive Plant Maintenance solution

Predictive Plant Maintenance Solution comprises equipment and sensors, gateway, cloud service, and management to sense, record, and provide actionable insights on the machine’s condition. Artificial intelligence, machine learning, and IoT always try to yield accurate results.

But before you buy a predictive plant maintenance solution, consider these 7 critical aspects of it to decide which predictive maintenance solution is right for you.

Easy-to-Use and intuitive for everybody

The ideal Predictive Maintenance solution must be easy to use for all, from onsite plant operators and technicians to the plant manager & plant head. It should be intuitive and user-friendly to be mainly accessible to everyone required. If you need a data scientist every time to decode the insights provided by this software, then you are set up for sudden asset failures due to delayed responses.

The right predictive plant maintenance solution can empower the onsite condition monitoring/ maintenance teams with the correct machine data at the right time for successful plant maintenance assessments with actionable insights.

Finding the root cause, not just alerts

Some Predictive Maintenance solutions indicate only alerts of anomalies, while the others yield insightful data alerts with what might be causing them. Those insights can be used to get a 360º condition of working equipment, and plant engineers can trace the root cause of the problems and plan a more effective solution. It also helps to distinguish the false alerts from the true ones.

For example: Just pointing out an issue with an exhaust fan of a kiln in a cement plant may lead to 1000 causes, but a solution that analyzes this further and points to a loose bearing that may be the cause can lead to a different level of agility for your maintenance teams.

Are the outcomes measurable or just hopeful?

Ensure that the maintenance technology brings you the results in some way or the other. And the results must be measurable and not hypothetical, which means you should be able to calculate the profits that the technology is bringing against its cost.

The average cost per hour of equipment downtime is $260,000. Look for a predictive maintenance solution that can save you the downtime cost and increase profits. Predictive maintenance can reduce machine downtime by 30%-50% and increase machine life by 20%-40%. (McKinsey)

Usable across assets and manufacturers

A plant usually has various types of equipment from multiple manufacturers and suppliers, depending upon the quality and cost. The Predictive Maintenance solution you are planning to install must easily integrate and comply with every piece of equipment in the plant- regardless of its age, type, and manufacturer.

Having different data collection mechanisms for different equipment is costly and leads to entropy & silos that obstruct the whole picture. Technology, along with human intelligence, functions to streamline complex processes and increase efficiency, and not the opposite. 

Experience around process plants

Process manufacturing plants differ from other industries in various aspects. Predictive maintenance solutions request historical data to function reliably, but process plants have limited historical machine data, making it difficult for the predictive solution to function properly. Make sure your vendor has experience working with process plants to tackle the situation constructively.


Deployment & scaling time 

One of the most popular hesitation in IoT-driven Plant maintenance deployments is the time taken to deploy the solution. If the deployment takes months, the internal enthusiasm built around the deployment dies down, and so does the ROI.

It is also essential that the deployment velocity is maintained when the solution is scaled up-whether from some machines to the entire plant or across plants.

Look for a predictive maintenance vendor that can integrate the solution in your plant and enable working within a few weeks and not months.

Predictive Maintenance in mining can cause many benefits – direct & indirect.


Predictive plant maintenance solutions save millions of dollars for manufacturing companies by predicting equipment health and indicating impending failures beforehand. Various predictive maintenance solution providers come with multiple packages, and hence choosing the right fit for your plant is important. Look for an easy-to-use and intuitive product that can comply with mixed assets from diverse manufacturers. At Infinite Uptime, we strive to transform the industrial & machine health diagnostics space. Our Predictive Maintenance solutions are used by hundreds of process plants globally, saving millions of hours of downtime, and improving the efficiency, scale & output of plants, one insight at a time.

Want to know more about how you can safeguard your machine’s health & reliability with Predictive Maintenance?
There are three main types: Reactive Maintenance, which fixes equipment after it fails; Preventative Maintenance, which follows scheduled check-ups to prevent failures; and Predictive Maintenance, which uses AI and IoT to monitor equipment in real-time and predict failures before they occur, reducing costs and downtime.
Predictive Maintenance allows for proactive equipment monitoring, predicting failures based on real-time data. This approach minimizes unplanned downtime, extends equipment life, and optimizes maintenance schedules, resulting in significant cost savings and improved operational efficiency compared to reactive and preventative strategies.
Predictive Maintenance enhances plant productivity by ensuring machines operate at peak efficiency with minimal downtime. It also promotes a safer working environment by preemptively addressing equipment issues, thereby reducing risks to plant workers and assets.
Choose a solution that is easy to use and intuitive for all plant personnel, integrates seamlessly with diverse equipment types and manufacturers, and provides actionable insights rather than just alerts. Ensure the solution offers measurable outcomes in terms of reduced downtime and increased equipment longevity.
IoT enables real-time data collection from sensors embedded in equipment, facilitating predictive analytics and condition monitoring. This data-driven approach allows plant managers to make informed decisions quickly, improving overall plant efficiency and reliability.
Look for a solution provider that offers quick deployment and scalability options. The time taken to implement the solution should be minimal to maintain momentum and ensure rapid ROI. Scalability should also be seamless, allowing for expansion from pilot phases to full plant integration without significant disruptions.
Condition Monitoring

Predictive Maintenance as a Service: A game-changer for Manufacturing.

Predictive Maintenance as a Service: A game-changer for Manufacturing.


Manufacturing encompasses a diverse and wide array of processes, industries, and raw materials. Yet all manufacturers everywhere share a common enemy: Unplanned Downtime, which harms productivity, asset health, brand reputation & dotted line.

Every year, top fortune 500 manufacturing enterprises lose almost 1 trillion to unplanned downtime, nearly 8% of their annual revenue. Here is how the introduction of Predictive Maintenance as a Service can be a gamechanger in the world of manufacturing & asset maintenance to curb unplanned downtime.

But to understand why Predictive Maintenance as a service is so revolutionary, let’s understand how asset maintenance was performed until a few years ago:

What are the types of Asset Maintenance practices in Manufacturing?

Reactive Maintenance: Reactive Maintenance means letting your machines run unchecked till they fail. Maintenance here is post-failure, as a reactive approach after the anomaly. While it saves you unnecessary downtime & maintenance costs for parts that don’t require servicing, it also means you risk machine failure anytime by being blind about your machine health.
Planned Maintenance or Preventive Maintenance: After the reactive maintenance approach resulted in constant fire-fighting for the plant manager, maintenance became a time-based activity, i.e., annual, bi-annual, based on their own and peer’s experiences. But often, it was noted that a planned downtime, although revealing nothing wrong with the asset, would still result in loss of productivity & profits. And sometimes, the machines would fail even before the planned period, so the problem persisted.

With the failure of both of these approaches to curb machine failure and thereby unplanned downtime in time, the industry looked forward to solutions like IoT & AI to power up maintenance with real-time insights. And that is what predictive maintenance as a service is all about.
Predictive Maintenance as a Service: Predictive Maintenance (PdM) relies on real-time monitoring of machine health using smart technologies like edge-computing, IIoT, data science, and analytics. Once an anomaly (w.r.t vibration, temperature, or acoustics) is detected, it is flagged off to the relevant plant supervisor for the next immediate action. A maintenance activity can thus be scheduled if something goes wrong while the maintenance expert can also decode the exact ‘something’. PdM enables the maintenance teams with necessary controls to extend equipment lifecycle, optimize the cost of maintenance, maximize machine uptime and amplify factory performance.

Types of machine maintenance in mining What Industries can Predictive Maintenance as a Service make the most impact on?

While any manufacturing plant- whether discrete or process-based can deliver a clear impact with Predictive Maintenance as a Service measures, the process-based manufacturing plants can truly thrive because of their unique workflow of interconnected processes.

Since the output of the process manufacturing plant depends on the previous steps completed in tandem, the stoppage of even a single machine can halt the entire production process. This is where predictive maintenance as a service can help by ensuring that the machine health issues are taken care of before they become serious.

Here are some examples of plants where Predictive Maintenance as a Service can save the day:

  • Cement plants
  • Steel plants
  • Metals & Mining
  • Oil & Gas Refineries
  • Power plants
  • Chemical plants
  • Pharmaceutical plants
  • Petrochemical plants

How can Predictive Maintenance as a Service be a gamechanger for Manufacturing?

Predictive Maintenance as a Service brings all the benefits of cutting-edge technology without the financial downside of capital intensiveness and sustainability. Here is how it can do magic for manufacturing plants:

Asset health & performance: In an asset-intensive industry like manufacturing, where the equipment is costly and used to the extreme, equipment & component replacement costs are prohibitively high. You can boost asset life, RUL (Remaining Useful Life), and Machine Uptime & Reliability by tackling asset issues before they get serious.

EHS & Compliance benefits:
Manufacturing plants consist of the most demanding working environments with toxic gases, material, and dangerous machines working furiously. It is no wonder that the regulatory guidelines get stringent now and then. It is also a risky working environment for the operators and other employees. Predictive maintenance as a service policy ensures no untoward accidents or compliance issues. Read more on our ATEX Certification.

OEE: Standing for Overall Equipment Effectiveness OEE is a globally prevalent metric that measures the productivity of a manufacturing asset. Calculated as a product of equipment availability, performance & quality of output produced, OEE is a benchmark for comparing the productivity of plants. The availability & performance of the machine depends on the maintenance & servicing when it is needed. According to a Deloitte study, a regular PM results in high OEE, Uptime & Reliability, compared to all types of maintenance.
Predictive Maintenance as a Service A game-changer for Manufacturing_OEE
Quality & Brand reputation: Regular Asset maintenance and machine health analysis can ensure that the machine performs at the top of its capacity. This will provide a high quality of the overall output. A fully functional plant producing quality output with minimal disruptions will also ensure a good brand image & reputation in the ecosystem.
Increased Employee Productivity: A well-functioning asset means that employees don’t have to fight fires caused due to last-minute machine failures. It also means quality and timely output, allowing them to be productive at what they do.

What Infinite Uptime’s Predictive Maintenance as a Service brings to the table?

The end-to-end Predictive Maintenance as a service by Infinite Uptime involves collecting data & computing the triaxial vibrations, temperature, and noise of the mechanical equipment in real-time via its patented edge computing system. The data is then monitored & analysed in real-time, and a machine health score is assigned. A machine with a lower health score is flagged to the plant supervisor or plant engineer with a diagnostic assessment score and the probable cause for the anomaly and a recommendation on improving the machine. This helps the maintenance teams to plan better and save critical downtime of machines which positively impacts the overall factory performance and productivity.


With real-time insights from interconnected assets being monitored and analysed instantly, predictive maintenance as a service provides massive power to the manufacturers without any drawbacks of a conventional maintenance solution. And that is when the true digital transformation will happen when data and insights combine to provide value.
Predictive Maintenance as a Service leverages IoT, AI, and real-time data analytics to monitor machine health continuously. It predicts potential failures before they occur, allowing proactive maintenance to prevent unplanned downtime and optimize equipment performance.
Industries such as cement, steel, metals & mining, oil & gas refineries, power plants, chemical plants, pharmaceutical plants, and petrochemical plants can benefit significantly. These industries rely heavily on continuous production processes where any downtime can impact productivity and profitability.
Predictive Maintenance as a Service utilizes real-time data and advanced analytics to predict equipment failures before they occur, unlike reactive or planned maintenance methods that rely on scheduled or post-failure interventions.
PdMaaS enhances asset health and performance by identifying and addressing issues before they lead to failures. It increases equipment reliability, extends asset lifespan, improves overall equipment effectiveness (OEE), and ensures compliance with safety and environmental regulations.

Predictive Maintenance as a Service reduces operational costs associated with unplanned downtime and reactive maintenance. It enhances quality control, boosts brand reputation, increases employee productivity, and provides real-time insights for informed decision-making in manufacturing operations.

Condition Monitoring

Digital Transformation – The Future of Predictive Maintenance

Digital Transformation – The Future of Predictive Maintenance


Digital Transformation or digitization is no longer just a hype or a concept. It has found a pertinent place in every facet of business and companies who want to build futuristic business model. The advent of these disruptive technologies is proving to be a game-changer and driving a digital revolution in the industrial and manufacturing space. Conventional manufacturing industries are rapidly transforming into digitally connected enterprises by adding OT (Operations Technology) & IT (Information Technology) muscle to their production processes, manufacturing capabilities and maintenance programs.

In simple words, mechanical data and insights generated by applying these technologies are helping industrial setups to control cost, improve speed to market, drive critical decisions, add customer-centricity, and create competitive advantages.

Challenges in the mining Applying Digital Transformation as a Service (DTaaS) in manufacturing industry

Predictive Maintenance (PdM) and Condition Monitoring (CM) are the most sought-after and top opportunities in the industrial ecosystem from a return-on-investment standpoint. Digital transformation in PdM & CM is the application of advanced computing technologies to enhance the overall machine efficiency, reliability, outcome and sustainability in manufacturing operations. It combines a collaborative approach of man-machine-technology ecosystem along with all its service components.

Benefits of DTaaS in Predictive Maintenance and Condition Monitoring

DTaaS provides reliability-engineering teams with a plethora of new tools & approaches to effective machine maintenance and opportunities to accelerate transformation and revenue growth for the top stakeholders. Here are some direct benefits of converting the data & insights from a digital system to operational performance:
  • Save 30-50% or more on maintenance cost
  • Increase machine uptime significantly
  • Improve visibility in maintenance programs and manufacturing processes with accurate machine data
  • Build a connected enterprise with a man-machine-technology ecosystem
  • Create a digital drive in your enterprise to achieve Industry 4.0 objectives

DTaaS influences multiple aspects of the industry irrespective of the business competency, production facility and promotes coherent integration with hardware and modern technology such as IoT, Artificial Intelligence, Machine Learning, Cloud Computing, etc. The cost-effective yet endless services signify improving existing processes, nurturing ideas, over-ruling cultural barriers, and providing a generic interface with the professionals.

The emerging service model enriches customer experiences and focuses on customer connectedness. The excellence of the services is directly related to data collection, vibration monitoring, predictive analytics, visualization and reporting. Developing a strong bond with the customer at a functional and business level to promote an adaptive and data-guided mindset encourages better alignment of the available infrastructure and resources.

Begin your Digital Transformation journey with Infinite Uptime

DTaaS resolves the complexity of Digital Transformation that depends on IT integrations, extraordinary consultations, considerable upfront costs, and the relative value-addition at different stages. 95% of industry experts agree that DTaaS in manufacturing is essential to create a broader business impact and create a deeper business relationship. Infinite Uptime has been extending its DTaaS services with Diagnostics to all the engineering and manufacturing industries.

The services comprise of monitoring, collecting and analysing mechanical (machine or equipment) data using a patented technology called, vEdge, an Industrial Data Enabler (IDE) and Industrial Data Analytics Platform (IDAP). This empowers the maintenance authorities, CBM experts and maintenance heads to perform in-depth analysis and diagnoses the root cause of discrepancies in the machine.

These real-time machine health diagnostics solutions enabled with connectivity, communication, machine analytics, insights and reporting are driving innovation in manufacturing. Industrial IOT, AI, ML, Cloud and data analytics is rapidly accelerating strategic C-Suite objectives like Smart Manufacturing, Industry 4.0 Transformation and creating a Digitally Connected Enterprise.

Looking for expert guidance on how you can start your digital transformation journey or worried whether this will benefit your business?
DTaaS integrates advanced computing technologies like IoT, AI, and cloud computing to enhance machine efficiency, reliability, and sustainability in manufacturing operations. It transforms traditional industries into digitally connected enterprises, optimizing maintenance programs and driving competitive advantages.
DTaaS enables reliability-engineering teams to save significantly on maintenance costs (up to 30-50%), increase machine uptime, and improve visibility into maintenance and manufacturing processes through accurate machine data. It fosters a connected ecosystem of man, machine, and technology, aligning with Industry 4.0 objectives.
DTaaS leverages IoT, Artificial Intelligence (AI), Machine Learning (ML), Cloud Computing, and advanced data analytics to enhance operational performance, optimize maintenance strategies, and facilitate predictive analytics for proactive decision-making in manufacturing.
DTaaS accelerates Industry 4.0 objectives by enabling smart manufacturing through real-time machine health diagnostics, predictive analytics, and seamless integration of hardware and digital technologies. It promotes a data-driven mindset and enhances business agility and customer connectedness.
DTaaS resolves complexities associated with digital transformation by offering cost-effective solutions, real-time insights, and strategic guidance. It supports broader business impacts, strengthens customer relationships, and drives innovation across engineering and manufacturing sectors.
Condition Monitoring

Understanding Machine Diagnostics and Remote Monitoring

Understanding Machine Diagnostics and Remote Monitoring

Understanding Machine Diagnostics and Remote Monitoring_blog_banner

Managing machine health and ensuring sustainable asset performance is an important control objective in manufacturing environments. In both discreet and process-based manufacturing industries, net plant productivity, operational costs, and return on assets (ROA) depend on how well the available assets are utilized. This makes monitoring and diagnosing faults in all available assets a high priority for maintenance teams.

This article will cover what machine diagnostics are and how remote monitoring enables maintenance teams to track machine conditions, detect existing or impending faults, isolate the root cause, and initiate procedures to restore machine health.

What is Machine Diagnostic?

Machine diagnostics is an equipment control measure that focuses on tracking machine conditions and detecting faults that can lead to machine breakdown. The process includes fault detection, followed by root cause analysis. Machine diagnostics is critical in rotating machinery that operates round-the-clock and is essential for undisrupted production in a manufacturing environment.

Diagnostic data is collected with the help of various offline and online techniques, depending upon the scope of the maintenance. This data is then analyzed using methods like high-resolution spectral analysis, wavelet analysis or transform, Fast Fourier Transform (FFT), Wigner-Ville Distribution, waveform analysis, etc. The analyzed data is used to determine the root cause of faults and recommend corrective actions to the maintenance and operation teams. 


For instance, a boom conveyor in a steel manufacturing plant can break down due to faults in motors, pulleys, or gearboxes. In a complex machine like this, it is difficult to determine what could have caused the breakdown and what kind of maintenance activity is required. With machine diagnostics, tri-axial vibration trends of the boom conveyor can be monitored to determine the root cause of machine breakdown. 

If the machine’s health is deteriorating due to misalignment or bearing defects in the gearbox, then data-backed maintenance action can be carried out to restore machine function. (Read the full case here.)

What is Remote Monitoring?


To make machine diagnostics efficient and empower maintenance teams with real-time data, machine health can’t be measured manually at sporadic intervals. There needs to be a system of continuous monitoring with complete data accessibility across the connected enterprise. This is achievable through remote monitoring of plant assets, using IoT-enabled technologies. 

Remote monitoring deploys one or multiple cloud-enabled solutions to remotely monitor machine conditions. Machine vibration data is collected in real time and relayed via the cloud to a centralized repository. Any anomalies in the machine vibrations are analyzed and compared with pre-defined specifications to determine whether a fault exists or building up within the equipment. The maintenance teams can get complete visibility of machine health from any time, anywhere; facilitating a planned shift to the predictive maintenance approach.


Remote monitoring services also help create safer working conditions for maintenance and production teams. Potentially hazardous conditions can be detected from a safe distance, without any manual intervention on the site. At the same time, all maintenance-related decisions can be guided by data-backed insights, leaving very little to chance.

Remote Condition Monitoring for Machine Diagnostics

The most preferred method of remote monitoring is Condition Based Monitoring (CBM) in manufacturing industries. Condition monitoring is a non-destructive and non-disruptive technique of monitoring plant equipment to determine its health or condition at any point in time. Remote condition monitoring determines the stability or possible deterioration of equipment condition. The rate of deterioration and where exactly is the equipment in its life cycle are also determined and conveyed to concerned stakeholders.

Commonly used condition monitoring techniques in manufacturing industries are:

  • Vibration Analysis: Measuring vibrations within a component, machinery, or machine group.
  • Oil Analysis: Analysing oil contamination, and machine wear to determine asset health.
  • Electrical Analysis: Analysing incoming power quality from the equipment, using motor current readings.
  • Ultrasonic Analysis: Detecting high-frequency sound waves through ultrasonic analysis.
  • Infrared Thermography: Detecting radiations and temperature changes using a thermal imager.

Of all these, vibration analysis is best suited for rotating equipment in process-based manufacturing environments. Abnormal machine vibrations are used to generate displacement, velocity, and acceleration maps, which in turn help in diagnosing the underlying causes of faults in the machine. Predictive analytical insights can be generated from this exercise which can support maintenance teams in planning focused repair and replacement events.


In sum, machine diagnostics and remote monitoring are instrumental in managing asset performance and improving overall plant reliability. Real-time machine diagnostics using sophisticated, IoT-enabled techniques can give maintenance teams better visibility of asset conditions. With remote monitoring, machine diagnostics can be performed in a cost-efficient and secure manner, giving improved data accessibility across the organization. As machine faults are predictively diagnosed, corrective actions can be taken before the incidence of actual machine breakdown.

Infinite Uptime offers responsively designed machine diagnostics, remote condition monitoring, and predictive maintenance solutions in diverse industries such as Cement, Steel, Mining and Metals, Tire, Paper, Automotive, Chemicals, FMCG, Oil and Gas, and more. To understand how predictive maintenance applies to your process plant and can help in achieving plant reliability, explore the comprehensive solutions of Infinite Uptime.

Machine diagnostics in manufacturing refers to the process of monitoring and analyzing machine conditions to detect faults or potential breakdowns. It involves collecting data through techniques like vibration analysis or spectral analysis to pinpoint the root cause of issues affecting machine health.
Machine diagnostics helps manufacturing industries by minimizing downtime through early fault detection and predictive maintenance. By identifying issues before they escalate, maintenance teams can plan proactive repairs, thereby optimizing asset performance and reducing operational costs.
Remote monitoring involves continuously monitoring machine conditions in real time using IoT-enabled technologies. It allows maintenance teams to access data from anywhere, facilitating proactive maintenance decisions based on real-time insights. Remote monitoring enhances the effectiveness of machine diagnostics by ensuring comprehensive and continuous visibility of asset health.
Common techniques include vibration analysis, oil analysis, electrical analysis, ultrasonic analysis, and infrared thermography. These methods help in detecting anomalies such as vibrations, contamination levels, electrical irregularities, and temperature changes, which are indicative of potential machine faults.
Remote condition monitoring offers benefits such as enhanced safety for maintenance teams, as potential hazards can be identified remotely. It also supports cost-efficient operations by enabling predictive maintenance strategies based on real-time data, thereby improving overall plant reliability and reducing unplanned downtime.
By continuously monitoring machine health and diagnosing faults early, these technologies help in maintaining optimal asset performance. Predictive maintenance driven by machine diagnostics and remote monitoring ensures that maintenance activities are proactive rather than reactive, leading to improved operational efficiency and extended equipment lifespan.
Industries such as Cement, Steel, Mining and Metals, Tire, Paper, Automotive, Chemicals, FMCG, Oil and Gas, among others, can benefit significantly from these solutions. The ability to monitor critical equipment remotely and predictively address maintenance needs is crucial for enhancing productivity and reliability across diverse industrial sectors.

Key features include real-time data accessibility, predictive analytical insights, and responsive design tailored to meet the specific needs of process plants. Infinite Uptime’s solutions enable seamless integration of machine diagnostics and remote monitoring into existing operational frameworks, ensuring continuous improvement in asset performance and reliability.