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IoT Predictive Maintenance: Prepare to Repair

Key Takeaways

  • The present models of manufacturing repairs and the disadvantages of each.

  • How IoT predictive maintenance can combine the strengths of current models.

  • A detailing of the IoT predictive maintenance process.

Bottom view of an ultrasonic sensor.

IoT predictive maintenance relies on sensors to measure the performance of parts over time.

In the ceaseless drive for efficiency, manufacturers are looking inward more than ever. System processes are awash in actionable data that had long gone unused. IoT predictive maintenance is a course correction in that sense: data from parts are collected and analyzed to understand better how and when components fail, simultaneously avoiding maintenance that is too frequent or infrequent. IoT systems are a relatively simple implementation that belies complex and sophisticated data collection, modeling, and analysis, providing crucial quality management enhancements for the modern age.

Comparison of Maintenance Styles

Reactive Maintenance

Predictive Maintenance

Preventive Maintenance


Maximizes usable service life out of components

Gives users insight into when parts are actively failing, optimizing service life and maintenance schedule

Uses manufacturer’s directions as a baseline for replacement Maintenance follows a schedule; downtime is not a surprise


Most costly to repair Unscheduled downtime Failure of one component could lead to damage to surrounding parts/equipment

Requires additional devices and system infrastructure Cloud-computing necessary in most cases due to size of dataset

Service life of components suffers and maintenance may be more frequent than necessary, equating to a loss of productivity and value

IoT Predictive Maintenance: A Scheduling Middle Ground

A crankshaft sensor partially wrapped around itself.

Electrical sensors can combine with mechanical parts – like in this crankshaft position sensor – to convey information about component or system health.

Time equals money, including manufacturing uptime. Avoiding unplanned downtime and maximizing the time between scheduled maintenance without endangering equipment helps production managers maximize efficiency. IoT predictive maintenance leans heavily on sensors that track early markers of degradation. For example, a sensor for an electromechanical part in a greater system may exhibit altered values revealing a malfunction. Over a large enough sample size, recent and historical data comparisons can indicate a significant deviation from the norm while accounting for other runtime variables. 

Predictive maintenance is an optimal middle ground between two styles of manufacturing management:

  • Reactive maintenance - Reactive maintenance is a failure-based system where repair or replacement only occurs when a system is no longer functional (or is malfunctioning to the point of disrupted service). Reactive maintenance theoretically extracts maximum value from equipment while minimizing downtime, but this assumes that failure doesn’t cause irreparable damage and that unplanned downtime will incur the same productivity losses as a scheduled maintenance period. This maintenance style is best suited for inexpensive and noncritical parts and equipment – e.g., a fluorescent bulb.
  • Preventive maintenance - This system is a time- or usage-based strategy that minimizes unplanned downtime with regular equipment maintenance. System engineers can plan maintenance timelines using manufacturer data on service life (like time/cycles to failure). Preventive maintenance will also want to factor in operating conditions like excess heat or moisture that may increase the degradation rate. In effect, this mode sacrifices some equipment service life and overall productivity to prevent unforeseen disruptions that can grow more complex without regular maintenance.

Predictive maintenance combines the strengths of both systems for maximum efficiency. By monitoring the performance of a part with sensors, predictive maintenance can detect signs of failure at the earliest possible point. At the same time, monitoring of components allows for verification of the complete service life while keeping maintenance to a minimum. Unoptimized maintenance schedules and wasteful replacement represent significant industry expenditures, both of which predictive maintenance curtails.

How IoT Systems Transform Device Metrics into User Guidance

The remaining half of IoT predictive maintenance pulls the concept together. For industrial settings, the Internet of Things (IoT) is a network that connects devices that were historically adrift in data analysis. A system may contain many components contributing to its functional success, but performance tracking may ignore several integral parts. By connecting sensors of a system or systems through a communication protocol, the immediate need for maintenance oversight lessens as manufacturing can respond dynamically to fluctuations in operating conditions.

IoT shares a symbiotic relationship with cloud computing. The sheer size and throughput of data in even modest IoT setups have outstripped the computational ability of most local area networks. Instead, data travels to a central repository better equipped to handle intensive data processing requests. Servicing the data this way also opens the door to improved analytics, which can uncover patterns, trends, and relationships.

The methodology for IoT predictive maintenance must combine data collection with sophisticated models for data analysis to optimize system efficiency:

  • Data collection/integration - Data collection/integration - Active data collection can come in three forms: logistical, sensing, and test. Sensing data measures the physical parameters, while logistical data contains information about the maintenance history of the component or system. Finally, test data is unique sensor data used to verify and refine the other two types. Signal processing is necessary to reveal the relationship between different components.
  • Modeling - After preparation, the data needs predictive modeling for interpretation. Diagnostic and prognostic models can analyze the remaining service life before failure or a decline in the output. By comparing the in-system performance of the component against some average lifetime, analysts can pinpoint when failure may occur (or if it already has). The new data can then refine future failure detection models.
  • Optimization - Human operators must interpret model output; the data and recommendations require some level of filtering for speedy implementation. Documentation might include work history reports or a projected maintenance schedule.
  • Presentation - Finally, information must be readily available and accessible. By collating analysis outputs into a central dashboard, users can quickly search through records to verify maintenance history, view outputs in real time, and study trends with an overall picture of the system’s health.

Cadence Systems Build Out IoT Functionality and So Much More

IoT predictive maintenance empowers the industry by reducing service disruptions to a just-in-time schedule. Not only can manufacturers reap the benefits of longer uptime cycles, but costly part replacement is kept to a minimum by utilization of the full-service life. Predictive maintenance starts with sensors to record and controllers to transmit system data for various analysis techniques; the design of components is critical to meeting the power and network protocol requirements. Electronic development teams have a new comprehensive solution with the  Allegro X Design Platform: a complete design environment for PCBs down to logical/physical implementation. Alongside the Allegro X Advanced Package Designer, design teams can layout chips using the same thermal and signal integrity analysis tools they’ve grown accustomed to at the board level.

Leading electronics providers rely on Cadence products to optimize power, space, and energy needs for a wide variety of market applications. To learn more about our innovative solutions, talk to our team of experts.