Determining When and Where Prognostics and Health Management (PHM) Should be Integrated into Manufacturing Operations

Determining When and Where Prognostics and Health Management (PHM) Should be Integrated into Manufacturing Operations

Investments in advanced health monitoring for factory equipment require a sound business case. Making this case should include detailed rationale and key considerations based on best practice guidelines for when and where heath monitoring should be used.  A strategic business case is needed to support the upfront investments to design and build a monitoring system that can enhance maintenance routines to reduce faults and failures that could compromise quality and delivery goals.  Prognostic and health management (PHM) is defined as “a maintenance and asset management approach utilizing signals, measurements, models, and algorithms to detect, assess, and track degraded health, and to predict failure progression.”  PHM does not only enable monitoring the health of individual equipment but, also that of subsystems, work cells, and potentially even the entire manufacturing process.  PHM technologies have been applied within large industries such as aerospace, energy, and process industries for the past decade.  However, small to medium size manufacturers often prefer user driven manual techniques due to implementation complexity and infrastructure requirements.  Overall equipment effectiveness (OEE) is a convenient metric on an asset’s capability to perform its specified function reliably, to the desired quality specifications, and on time.  As such, OEE can be used as a baseline to build the business case in considering where operational efficiencies can be improved and how the health monitoring solution can be cost effective.  A manufacturer typically has a data collection process paired with the measurement and monitoring of the operational parameters that affect equipment availability, productivity, and quality of operations. The captured historical process data, along with maintenance work requests, can be used to identify the process pain points that may be ripe for implementing PHM technologies.

Monitoring the operational performance of individual pieces of equipment can provide manufacturers an intuitive way to assess what can go wrong in a manufacturing process. It also makes sense to pay attention to production-critical equipment with a history of failures, and those elements that undergo accelerated usage or excessive duty cycles.  Some equipment vendors provide health-ready instrumentation with embedded sensors for detecting failures. Vendors may also offer comprehensive monitoring strategies associated with the equipment’s critical failure modes.  Advances in internet of things (IoT) technology, coupled with network communications and increased processing power, have made enhanced sensors and analytical strategies for detecting or predicting equipment failures more achievable.  Numerous manufacturing equipment components, from rotating machinery to robotic systems, are more effectively monitored using IoT and machine learning techniques for statistical processing, historical trending, the application of physics and empirical models, sensor fusion, event correlation, expert systems, and model-based reasoning.

An assessment of overall process health can produce actionable awareness regarding availability, profitability, quality, sustainability, and reliability of a manufacturing process.  This information is also useful for establishing proactive, condition-based, or predictive maintenance strategies.  Faster processing platforms, increased memory storage, and increased prevalence of distributed computing platforms provide additional opportunities for leveraging computationally-intensive strategies along with model-based prediction and reasoning. These technological improvements make it possible for manufacturers to realize the value of real-time awareness of manufacturing effectiveness based on process performance indicators.

The incorporation of PHM into manufacturing is having a positive effect on equipment and plant maintenance. Maintenance policies have typically been based on the performance of routine maintenance actions that attempt to extend equipment life and minimize the likelihood of equipment failure.  Scheduled maintenance and other preventive methods have long been the norm, and maintenance objectives have been achieved through regular equipment inspections and scheduled maintenance at predetermined intervals based on operational time, cycles, units, etc.  

Asset condition monitoring (ACM) is a technique used to leverage health monitoring and data processing functions inherent within the assets. It is comprised of data acquisition, integration, contextualization, data exchange, and tools and processes to manage and restore the asset or work cell to a healthy state. These functional elements are required to deliver a complete solution and drive the operational and economic benefits targeted by a manufacturer.  ACM redirects maintenance policies toward condition-based maintenance or predictive maintenance. These strategies require monitoring and managing of the condition of equipment assets to minimize disruptions to manufacturing operations due to equipment downtime.  

With the advances in IoT, the author believes that data collection, data manipulation, and fault detection can be done in a fraction of the time required by industry just a few years ago. The IoT platforms can integrate the machine health with the overall factory process, unit number, work instructions, and engineering changes. This integrated approach would be very effective in advancing PHM as a useful tool within the manufacturing operations management function.  

The author is a member of the ASME Subcommittee  on Monitoring, Diagnostics, and Prognostics for Advanced Manufacturing which is developing industry guidelines to assist enterprises (of any size) in making informed decisions regarding the addition and integration of PHM technology into factory equipment assets within existing or new manufacturing operations.

The guidelines will include recommended best practice for: 1) Identifying areas for improving operational efficiencies; 2) Defining operational use cases linked to desired cost benefits, safety and environmental considerations, and operational improvements; 3) establishing baseline of current maintenance practices and health-ready capability levels; and 4) Implementing cost-effective equipment asset condition management strategies and measuring their effectiveness in improving operational efficiencies.