Don’t Miss Out on Predictive Maintenance through Machine Monitoring!


The importance of predictive maintenance cannot be understated. Predictive maintenance in manufacturing leverages data for safer, cost-effective operations by foreseeing maintenance needs, enhancing safety, and preventing downtime. Using machine condition monitoring software can help in increasing asset life, enabling safety compliance, and taking preemptive corrective action.

In this article, we explore how a machine monitoring platform for predictive maintenance helps manufacturing companies by leveraging key manufacturing process data.

Why do you need a predictive machine monitoring software?

Utilizing a machine monitoring solution for predictive maintenance offers numerous advantages for your manufacturing facility. Let’s look at them:

Real-time monitoring capabilities

An equipment monitoring platform offers real-time monitoring capabilities for machines to track their condition and performance. The data can then be compared against predetermined thresholds and baselines to detect any abnormalities or deviations in equipment behavior. Some useful metrics provided by these solutions include Mean Time Between Failures (MTBF), Mean Time to Failure (MTTF), Remaining Useful Life (RUL), Failure Rate, and Failure Modes and Effects Analysis (FMEA). They are useful in estimating machinery reliability, quantifying the likelihood of failure, and developing proactive strategies to avoid common failures.

Workflow optimization

An overwhelming 82% of companies have experienced at least one unplanned downtime incident over the past three years. One of the key features of a machine monitoring system is workflow optimization, which streamlines processes, reduces downtime, and improves efficiency. The analysis of real-time machine data in predictive maintenance manufacturing helps in tracking machine performance, generating predictive alerts and notifications, and enabling better communication and collaboration among maintenance teams, operators, and other stakeholders.

Let us take the example of a cement mill, which is the core component of the cement manufacturing process. The cement mill grinds and crushes clinker into a fine powder, which, after processing, is shipped to customers based on their desired quality and fineness of the cement. A machine monitoring platform or solution, in this case, may track parameters such as motor current, vibration, temperature, material flow, pressure, exhaust gas emissions, and dust collection system efficiency. For instance, by continuously monitoring vibration levels, the tool can predict the remaining useful life of critical components. This enables maintenance teams to plan preventive maintenance activities during scheduled downtime, avoiding unplanned stoppages, and optimizing maintenance schedules for the cement mill. This, in turn, helps in workflow optimization.

Improved safety conditions

Using a machine monitoring solution can help reduce the occurrence of major equipment repairs and improve the management of assets, leading to longevity. The use of machine learning algorithms and advanced analytics helps analyze real-time machine data to detect early warning signs in equipment performance and address them before any major failures. This, in turn, helps reduce the risk of replacements or expensive repairs and minimizes equipment downtime. Early detection of issues also allows for immediate intervention before a situation escalates into a safety incident.

Continuing the example of a cement mill. Any issues with its operation can result in safety risks. For instance, monitoring the current drawn by the cement mill's motor is crucial. A sudden increase in motor current could indicate a blockage in the mill or a mechanical issue. If left unchecked, this could lead to excessive heat generation, causing a motor failure or a fire hazard. With an equipment monitoring platform with sensors to track these parameters in real-time, cement manufacturers can avert a situation like this.

Enhanced product quality

Predictive maintenance in manufacturing helps in monitoring equipment performance and identifying potential flaws that ensure the right functioning of the equipment and prevents quality issues. Integrating equipment monitoring software with quality control systems helps in controlling critical parameters by capturing and analyzing machine data from various sensors and getting real-time insights into machine performance. This also helps in impeding ementing quality control measures. For instance, if a parameter like temperature exceeds the normal range, you can raise an alarm to notify operators or QC personnel to take corrective actions and prevent quality issues.

Improved equipment reliability

Machine monitoring systems enable condition-based maintenance that provides insights into equipment conditions and schedule maintenance actions based on resources, equipment conditions, and maintenance efforts. The software also helps in analyzing historical data and patterns to predict potential failures in equipment performance. This helps address issues based on equipment reliability and plan maintenance activities, which in turn helps minimize unexpected breakdowns and improves equipment reliability.

For example, bearings in a cement mill are critical components responsible for supporting the rotating shafts and reducing friction during the grinding process. Monitoring the vibration levels of these bearings is essential to assess their health and condition. Increased vibration levels in the bearing can indicate problems like misalignment, wear, or lubrication issues.

Types of issues a machine monitoring platform can detect

Now, you may think about what kind of issues a machine condition monitoring software can help your manufacturing facility detect. Here's a list of some anomalies/issues:

  • Anomalies and failure: The data from sensors when fed into the solution can help you detect and manage anomalies and equipment failures. Some examples include unexpected temperature changes or heat patterns due to lubrication issues, cooling system problems, component failures, or electrical faults.

  • Process inefficiencies: Lack of process standardization, use of outdated technology, and an absence of a culture of continuous improvement can result in process inefficiencies. The equipment monitoring platform helps address process inefficiencies by providing real-time data visibility, performance metrics, and predictive insights, enabling businesses to identify areas of inefficiencies and implement targeted improvements for increased productivity.

  • Inventory-related issues: Lack of real-time visibility into inventory levels and movements and the absence of demand forecasting can cause stockouts (insufficient inventory) or overstocking (excessive inventory). Equipment monitoring platforms are equipped with features that help analyze historical production data and consumption patterns to forecast demand more accurately, which can help in inventory management.

  • Lack of standardization: Inconsistent naming conventions and classification of spare parts across different departments or locations can be challenging when identifying OEM parts and make it difficult to schedule routine maintenance. Equipment monitoring helps digitize parts and maintain service catalogs to enable proactive machine maintenance.

  • Energy consumption deviations: Equipment monitoring software helps track the energy consumption of machines to identify any deviations from the expected energy usage. This helps in finding any malfunctions, inefficiencies, or component tears.

  • Error messages and fault codes: In the manufacturing sector, many machines are equipped with diagnostic systems that generate error messages or fault codes upon detection of anomalies. A machine monitoring solution can read this information from sensor data to provide real-time updates to the team upon the detection of anomalies. The early detection and resolution of these errors can help reduce risks related to machine performance.


In conclusion, embracing predictive maintenance through machine monitoring software is not just a strategic choice; it's a crucial necessity for the modern manufacturing landscape. The ability to harness real-time data for predictive insights revolutionizes workflow optimization, enhances safety conditions, ensures product quality, and improves equipment reliability. The illustrated example of a cement mill highlights how proactive monitoring can prevent safety risks, optimize maintenance schedules, and ultimately contribute to uninterrupted operations. As you consider the myriad advantages outlined in this article, the imperative question arises: Why wait to transform your manufacturing facility's performance?

Take the proactive step towards a future of efficiency, safety, and reliability by integrating iDataOps.