eWorkOrders CMMS Predictive Maintenance analyzes trends from sensors, meter readings, and history to flag issues before failure. Automated alerts and data-driven triggers help teams prioritize repairs, cut unplanned downtime, and extend asset life.
The eWorkOrders Predictive Maintenance Program gives organizations the tools to anticipate when equipment will need maintenance or replacement. Using sensor-based condition monitoring, it analyzes equipment performance and detects potential issues before they lead to failures. eWorkOrders CMMS automatically creates work orders when assets fall outside defined parameters, sending real-time alerts to technicians. By integrating with solutions like AssetWatch, Augury, DiagRAMS, Sensoteq, MachineMetrics, ReliaSol, and AccuPredict, eWorkOrders helps teams act proactively—reducing downtime and extending asset life.
What is Predictive Maintenance
Predictive maintenance (PdM) is a proactive maintenance strategy that tracks and monitors the performance and condition of equipment during normal operation. These monitoring tools detect various deterioration signs, anomalies, and equipment performance issues. Based on these measurements, maintenance work can be done just before a failure happens. Organizations must prioritize resilient maintenance strategies to stay agile during disruptions.
Real Results – From Real Users
Aaron
eWorkOrders: Best CMMS for Easy Work Orders & Accurate Inventory
Creating and monitoring work orders is very intuitive and valuable. The ability to verify what work was done and what parts were used is priceless. The inventory and purchase orders are intuitive and ...
eWorkOrders: Best CMMS for Easy Work Orders & Accurate Inventory
Creating and monitoring work orders is very intuitive and valuable. The ability to verify what work was done and what parts were used is priceless. The inventory and purchase orders are intuitive and a must as well. Being able to track on-hand inventory and how it flows is extremely valuable.
We chose eWorkOrders due to its superior ability to record the purchase and tracking of inventory. Also, the work order information was much more detailed and easy to use than anything on the market. The tech support is amazing as well! They ALWAYS respond to any issue or suggestion that we have in a timely and professional manner.
The implementation of this system has allowed us to document the work we perform on our plant equipment, both for maintenance and for safety records. We can now historically track all parts used and time spent on assets. We also have detailed records of the company assets as well.
The inventory portion allows us to order and track inventory as well as accurately cost what we use and check vendor pricing.
Aaron
Nick
eWorkOrders CMMS Boosted Maintenance and Cut Downtime at 5 Plants
On behave of The OPs VP, Myself, the Managers and the Maintenance teams across 5 very different plants, thank you all for bringing our PM programs from nearly non-existent in some cases, to a full-fle...
eWorkOrders CMMS Boosted Maintenance and Cut Downtime at 5 Plants
On behave of The OPs VP, Myself, the Managers and the Maintenance teams across 5 very different plants, thank you all for bringing our PM programs from nearly non-existent in some cases, to a full-fledged “well-oiled machine” also Special Thanks to the team for all his hard work and time spent guiding us and the Hrs spent teaching us and always giving us the right tools we need to succeed!! I could not imagine a better group of guys to work with than these
The System has drastically changed the way our maintenance team operates! cutting down on downtime and making sure that our teams are out in the field and focusing on the issues that really keep our plants running, and minimizing breakdowns by scheduling maintenance rather than waiting around for things to break.
Nick
Explore Additional Testimonials & Case Studies
Predictive vs. Preventive Maintenance
Predictive Maintenance monitors the performance and condition of equipment during normal production operations. Predictive Maintenance estimates the exact moment of a failure, and repairs can be scheduled when necessary. This is a cost-effective approach with minimal impact on production.
Preventive Maintenance tasks are completed based on a recurring time schedule or a given amount of usage or cycles. A planned and scheduled maintenance routine is put in place to extend the life of assets and reduce downtime. The maintenance is performed on predetermined assumptions, based on manufacturers’ recommendations or history.
Predictive Maintenance Objective
The primary objective of predictive maintenance is to anticipate equipment failures before they occur, allowing maintenance teams to take corrective action at the ideal time. By using data from sensors, inspections, and system trends, organizations can plan maintenance only when needed—reducing unplanned downtime, preventing costly breakdowns, and extending the useful life of critical assets.
The goal isn’t to perform maintenance more often, but more intelligently—based on real conditions rather than fixed schedules. Predictive maintenance helps align maintenance activities with actual asset health, optimizing performance, safety, and cost efficiency.
How Predictive Maintenance Works
Predictive Maintenance (PdM) depends on condition monitoring, which continuously collects and analyzes data from equipment during normal operations to ensure optimal performance and reliability.
There are three main elements that allow PdM to track asset conditions and alert technicians about projected equipment failures:
- Real-Time Tracking: Each piece of equipment is monitored through installed sensors that capture data on equipment health, deterioration, and performance.
- Internet of Things (IoT) Connectivity: IoT technology collects and shares data, allowing assets to communicate, work together, analyze conditions, and recommend corrective actions automatically.
- Predictive Data Analysis: Collected data is analyzed using predictive algorithms that identify trends and determine when an asset will likely require servicing, repair, or replacement.
Sensors transmit this data via wireless protocols (such as Wi-Fi, LoRaWAN, or Bluetooth) to cloud or edge analytics platforms. There, AI and machine learning algorithms process the data to predict potential failures days or weeks in advance.
Common use cases include manufacturing (vibration and temperature monitoring for assembly lines) and the energy sector (pressure and oil condition analysis for turbines). Integration with a Computerized Maintenance Management System (CMMS) like eWorkOrders automates alerts and work orders, ensuring quick and proactive maintenance response.
For implementation, most organizations start with vibration and temperature sensors, as these cover roughly 80% of rotating equipment faults. Over time, additional monitoring such as pressure, oil quality, and energy consumption can be added for deeper insights and optimization.
What Predictive Maintenance Sensors Monitor
Predictive maintenance (PdM) sensors are IoT-enabled devices that collect real-time data from industrial equipment and assets to detect early signs of failure, optimize maintenance schedules, and minimize downtime. These sensors monitor a wide range of physical and operational parameters using techniques such as vibration analysis, thermal imaging, and data analytics to identify trends or anomalies. The collected data is then fed into systems like eWorkOrders CMMS, where AI and machine learning algorithms generate predictive insights and automate maintenance actions.
Common Parameters That PdM Sensors Monitor Include:
- Vibration: Detects imbalances, misalignment, or bearing wear in rotating machinery (e.g., motors, pumps, fans). Often uses accelerometers for early fault detection in production lines.
- Temperature: Tracks heat levels in components (e.g., bearings, motors) to identify overheating caused by friction or electrical issues. Infrared or thermocouple sensors help prevent thermal failure.
- Pressure: Monitors fluid or gas pressure in systems (e.g., pipelines, compressors) to detect leaks, blockages, or pump failures — critical for hydraulic and pneumatic equipment.
- Oil & Lubrication Quality: Analyzes oil degradation, contamination, and particle levels in engines and gear systems to predict lubrication needs and reduce wear.
- Sound / Ultrasound: Captures high-frequency acoustic signals to identify leaks, cavitation, or electrical arcing before visible damage occurs. Commonly used for valves, bearings, and electrical panels.
- Humidity & Environmental Conditions: Measures moisture levels to prevent corrosion or mold in assets such as HVAC systems or storage tanks.
- Energy Consumption / Power: Tracks electrical draw or current to detect inefficiencies, overloads, or failing motors. Motor circuit analysis can flag unexpected power spikes.
- Speed & Rotation: Monitors RPM or rotational velocity in turbines and pumps to identify slowdowns due to wear or blockages.
- Acoustic Emissions: Detects subtle noise patterns from material stress or cracks in structures (e.g., bridges, pipes).
- Strain & Load: Measures mechanical stress on beams or frames to predict fatigue in load-bearing assets.
- Flow Rate: Tracks fluid or gas flow in pipelines to detect restrictions or leaks in process industries.
- Position & Movement: Uses encoders and displacement sensors to monitor alignment, positioning, or motion in conveyor systems and robotics.
Predictive Maintenance/Condition Monitoring Techniques
There are numerous condition-monitoring devices and techniques that can be used for effectively predicting failure, as well as providing advanced warnings for maintenance teams. Some of them include:
Infrared Thermography / Temperature Measurement
Infrared thermography is used to detect heat variations in machines and equipment. By capturing thermal images, infrared cameras help identify overheating components, allowing technicians to address potential failures before they escalate.
Ultrasonic Monitoring / Acoustic Analysis
This method uses high-frequency sound waves to assess the condition of bearings, rotating parts, and other machinery. It helps detect leaks, gear faults, and lubrication issues that could lead to equipment failure.
Vibration Analysis / Dynamic Monitoring
Vibration analysis is essential for high-speed rotating equipment, as it measures vibration patterns to assess a machine’s health. By analyzing these signals, technicians can detect imbalances, misalignments, or worn components before they cause major breakdowns.
Oil Analysis / Tribology
By examining oil samples, technicians can determine the presence of contaminants, metal particles, or signs of wear. This helps ensure proper lubrication and prolongs the life of mechanical components.
Laser Interferometry
A precision measurement technique that uses laser-generated wavelengths to detect even the smallest changes in wave displacement. This method is valuable for ensuring structural integrity and component alignment.
Motor Circuit Analysis
This process involves computerized testing of electric motors to evaluate their overall condition. It helps identify faults, insulation breakdowns, and early signs of electrical failure, ensuring reliability.
Radiography / Neutron Imaging
This technique uses radiation to inspect internal structures and detect hidden defects in machinery. It provides a non-destructive way to assess material integrity and prevent failures due to unseen damage.
What Circumstances Call For Predictive Maintenance?
Predictive maintenance (PdM) is best suited for equipment that plays a critical role in operations and has failure modes that can be detected or forecast through regular condition monitoring. These are typically assets where even a short downtime would cause significant production, safety, or financial impact.
On the other hand, predictive maintenance is not ideal for assets that are non-critical, inexpensive to replace, or have unpredictable failure patterns that make condition monitoring impractical or cost-ineffective. In such cases, preventive or run-to-failure strategies may be more efficient.
What Circumstances Call for Predictive Maintenance?
Predictive maintenance is most valuable for equipment that performs critical operational functions and has failure patterns that can be measured or predicted through condition-based monitoring. It’s commonly used by maintenance managers, reliability engineers, and asset management teams in industries such as manufacturing, transportation, energy, utilities, and facilities management—where even a short period of downtime can affect production, safety, or compliance.
By leveraging real-time sensor data, analytics, and non-destructive testing (NDT) techniques—including vibration analysis, thermography, and ultrasound inspections—organizations can detect early signs of wear or imbalance and schedule maintenance only when needed. This targeted approach reduces unplanned downtime, extends asset life, and improves overall operational efficiency.
Predictive maintenance is less practical for non-critical assets or equipment with unpredictable or low-impact failure modes, where preventive or run-to-failure strategies may offer better cost-effectiveness.
A Few Examples of Predictive Maintenance
Prevention of Power Outages
Power outages can be extremely inconvenient for those affected, and they can even be fatal in places like hospitals or assisted care facilities. They can be prevented by using predictive maintenance technology, which allows for early detection. Using cloud-based computers and artificial intelligence, sensors offer information about assets. Companies in the energy sector are informed by this knowledge when equipment failure is most likely to occur.
Building Management
With the aid of environmental monitoring and software for ventilation and energy management, buildings can be managed and controlled remotely. By using sensors tailored to the desired result, owners and managers can also control the temperature of the building environment and monitor humidity or moisture. The sensors provide the data to cloud-based data analysis tools, which enable you to spot anomalies or changes over time and schedule maintenance as necessary. This kind of monitoring can reduce the building’s overall energy costs. Building Management Since a manufacturing plant tends to have many costly assets and valuable equipment, they might invest in infrared imagers to monitor aspects of assets, such as temperature, to prevent overheating. This predictive maintenance system helps plants avoid overusing essential equipment, pushing machinery to the point of disruptive breakdowns.
Benefits
- Fewer asset failures result in reduced downtime.
- Reduced total labor time and cost spent on equipment maintenance.
- Automatic insights into your data.
- Control of spare parts inventory
- Improves worker and environmental safety.
- Increases employee efficiency.
- Increases production and ROI with properly maintained equipment.
Remote Monitoring
Remote monitoring allows maintenance teams to track asset performance and operating conditions in real time—without needing to be on site. By connecting IoT sensors and smart devices to your equipment, data such as temperature, vibration, pressure, or runtime is automatically captured and sent directly into eWorkOrders CMMS.
When conditions fall outside predefined thresholds, eWorkOrders automatically generates a work order or alert, ensuring that technicians are notified immediately for inspection or repair. This eliminates manual data entry, improves response time, and helps prevent costly downtime.
By combining IoT monitoring and CMMS automation, organizations gain continuous visibility into asset health, enabling proactive, data-driven maintenance decisions that reduce risk and extend equipment life.
Reporting
With predictive maintenance, data becomes insight. Sensor readings collected through IoT devices are automatically stored in eWorkOrders CMMS, where teams can visualize trends, generate reports, and export data for analysis. Graphs and dashboards display asset performance over time, helping maintenance managers identify abnormal patterns, track mean time between failures (MTBF), and forecast maintenance needs.
Reports can be filtered by date range, asset, location, or work order type, giving organizations a complete record of predictive maintenance activities and asset history. These detailed reports not only simplify audits and compliance but also provide the data foundation for smarter, reliability-focused decisions.
Additional Expense
Implementing a Predictive Maintenance (PdM) program often involves upfront investments in monitoring technology and training. Depending on your operations, this may include purchasing or integrating hardware such as vibration sensors, infrared thermography cameras, oil analysis kits, or ultrasonic monitoring tools to collect accurate equipment data.
In addition to hardware, organizations typically invest in employee training to ensure maintenance teams can correctly operate monitoring devices, interpret analysis results, and apply the insights to maintenance planning. While these costs vary based on scale and industry, the long-term benefits—reduced unplanned downtime, extended asset life, and improved reliability—often outweigh the initial expense.
Predictive Maintenance and CMMS
As businesses move from reactive to proactive to predictive maintenance, computerized maintenance management software (CMMS) plays a critical part to help facilitate predictive maintenance.
To be successful PdM requires the right balance of technology and human interaction. CMMS makes the process easier and here is why:
- CMMS is the engine that drives the PdM functionality. All of the information on asset performance that has been gathered and stored throughout the years in your CMMS is a starting point and the initial dataset before PdM implementation.
- CMMS integrates with PdM technology to generate alerts and work orders. With condition-monitoring sensor integration, some CMMS can automatically create an alert or generate a work order whenever sensors detect that an asset is operating outside predefined parameters. These alerts prompt the maintenance team to take preventive actions before the machine fails and causes unexpected downtime.
- CMMS is a centralized system that gathers and stores all of the information into one centralized platform that is accessible at any time from anywhere.
Predictive Maintenance and Return on Investment (ROI)
Implementing a predictive maintenance program requires a significant investment in money, resources, and training. In taking these things into consideration the initial investment into predictive maintenance’s return on investment (ROI) far outweighs any of these costs.
The reasons why:
- Reactive maintenance costs, resource time, loss of productivity, inventory backlog, delays in production, equipment downtime, and more are all hitting your bottom line.
- Having access to more accurate data gives you the ability to extend equipment life and improve the efficiency of maintenance operations.
eWorkOrders CMMS Predictive Maintenance
Predictive Maintenance (PdM) gives you the ability to predict failures and monitor performance on your most important assets. While the costs of investing in PdM technology may seem to be very high, over time this solution can provide significant ROI cost savings and better machine performance.
Linking the condition monitoring data to your CMMS allows for quicker dispatching of technicians, making it easier for repairs to get done faster. With eWorkOrders CMMS Predictive Maintenance you can define equipment operation boundaries, import the readings, graph results and automatically trigger an email to generate a work order when the readings go outside of the set boundaries. Having all of your data stored within a CMMS can help improve asset reliability, reduce costs and increase the efficiency of your maintenance operations.
If you are interested in learning about Predictive Maintenance or any of the other CMMS features, please feel free to contact one of our account executives, who will answer your questions and provide you with a free demo
From reactive repairs to AI-powered planning, this guide shows the full evolution of maintenance.
