preventative maintenance Archives

Digitalizing Manufacturing: Work Instructions and Sensor Feedback

Digital work instructions are becoming a game-changer in the increasingly fast-paced manufacturing world. They offer many benefits that enhance efficiency, reduce errors, and foster collaboration.

The digital advantage

Digital work instructions offer real-time updates, ensuring that all operators can access the latest version, even across various production facilities. This eliminates costly mistakes caused by outdated instructions or checklists.

These instructions are often part of larger workflows or Standard Operating Procedures (SOPs). Digital solutions can enable the seamless initiation of related procedures. For instance, if an operator identifies a machine issue, they can launch a workflow directly from the system for other team members or external workers.

Seamless integration

Digital work instructions integrate with planning systems, providing real-time visibility into expectations. Integrating ERP, MES, or LIMS systems allows data exchange and automated report generation.

Interactive feedback

Digital work instructions facilitate responsive interactions, unlike paper-based ones. When a threshold value is exceeded, the system can immediately alert the operator, who can notify supervisors or promptly follow additional instructions.

Automatic logging

Digital systems automatically record executed procedures, providing invaluable for compliance audits and continuous improvement initiatives.

Visual enhancement

Work instructions are often enhanced with multimedia elements such as images, videos, or 3D models. These visual aids enhance communication and comprehension and often reduce operator errors.

The future of manufacturing: sensor feedback

Sensors provide live data on equipment performance, environmental conditions, and safety parameters. This allows operators to receive immediate feedback for proactive adjustments, timely preventive actions, and “predictive maintenance” by monitoring wear and tear. Predictive maintenance helps schedule maintenance before critical failures occur, minimizing downtime, extending equipment lifespan, and ensuring efficient use of resources.

Additionally, sensors can detect deviations from optimal conditions, enabling operators to promptly address issues and maintain consistent product quality.

In conclusion, digital work instructions streamline processes, foster collaboration, and empower operators. When integrated with sensor feedback, businesses can gain efficiency and accuracy and, for a moment, enjoy that ever-elusive competitive edge in today’s rapidly evolving business environment.

Getting Started With Condition Monitoring

What is condition monitoring?

Unplanned downtime is consistently identified as one of the top manufacturing issues. Condition monitoring can offer a fairly simple way to start addressing this issue and helps users become more proactive in addressing and preventing impending failures of critical equipment by using data to anticipate problems.

There are four common maintenance approaches: reactive, preventative, condition-based, and predictive. Each has different cost-benefit tradeoffs, and it may be appropriate to use multiple approaches depending on the range of equipment in a facility. In general, the reactive and preventative approaches have significant drawbacks when used on critical equipment and when unplanned downtime is a major concern.

Condition-based monitoring and predictive maintenance (which uses condition-based sensors, tools, and data) offer approaches that can proactively warn of impending failure and are especially relevant to important equipment.

- Reactive: “Run until it breaks” might be used on non-critical, low-value assets, but is highly risky to apply to important components, where costs of repair and costs of downtime are high.
- Preventative: “Maintain at regular intervals, whether the asset needs it or not” might be appropriate when failures are age-related, but it may be that costly maintenance is being done on assets that do not need it.
- Condition-based: “Monitor limits on relevant indicators” can address failures regardless of whether they are age-based or random and monitors changes in one or more potential failure indicators, such as vibration, temperature, current/voltage, pressure, etc.
- Predictive maintenance and analysis: Attempt to learn from machine performance (condition-based data) to predict failure.

Condition monitoring provides warnings about faults and makes it possible to schedule repairs without unplanned downtime and lost production. It focuses on using sensors to monitor the status and health of machines, plants, or individual components (bearings, motors, fans, etc.) and then transmitting this data to control and/or supervisory systems for analysis and action. Continuous condition monitoring aims to detect changes and anomalies and can help customers record long-term trends and statistical evaluation – an entry point into predictive maintenance and predictive analytics.

How condition monitoring works

Typically, as a failure progresses, different indicators emerge (vibration, temperature, change in pressure & flow, etc.), and monitoring these can allow a more proactive approach than reactive or predictive maintenance. The Potential-Functional (“P-F”) Curve provides an example of the lifecycle of a failure:

Warning and alarm limits for the selected indicator(s) are set and when the limits are reached action can be taken. The limits can be set based on recommendations from the equipment manufacturer, ISO 10816-3 guidelines, or test data gathered from the machine. Over time, the data gathered can be analyzed to modify the limits and can be used as the basis for predictive maintenance and analysis.

When an alarm is triggered the maintenance staff can investigate and address the issue in a proactive manner – whether a simple task such as lubrication or minor adjustment, or a more critical part replacement – generally with time to schedule the activity during a planned downtime, rather than in the middle of production.

How to get started

We suggest you start with a small pilot system:

- Perhaps use a demo system, portable, or temporary tool.
- Set the initial alarm/warning limits based on ISO standards, manufacturer recommendations, or experience with similar machines.
- Gather data and look for insights.
- Modify limits based on data and consider using predictive maintenance software/tools for deeper analysis.
- Create buy-in with maintenance teams and the leadership team.
- Document the positive impacts of the changes and discuss them often.
- Grow the system over time.

Once you are ready to expand, an article in Control Engineering magazine provides advice on a process we endorse, including:

1. Conduct a criticality analysis: Which assets are most critical and have the most impact if they fail?
2. Identify probable failures the asset will experience: How has it failed in the past? What has happened to similar equipment? Does the manufacturer have recommendations?
3. Decide on the technology best suited to detect each failure mode: Do you need to monitor a device, machine, or complete facility? What are the most appropriate indicators and the sensors to detect them? Do you need continuous or one-time monitoring? Where is the data needed and what is the best way to transfer it?
4. Trend and analyze the data to plan and execute maintenance actions at the most advantageous times: How will you visualize the data? Do you want to use software to do analysis for you? Are there obvious trends and conclusions to be made?

Getting started with condition monitoring can seem challenging and complicated. By starting small you can learn what does and doesn’t work and take a more proactive approach to maintenance as you spread condition monitoring throughout your facility.

Predictive Maintenance vs. Predictive Analytics, What’s the Difference?

With more and more customers getting onboard with IIoT applications in their plants, a new era of efficiency is lurking around the corner. Automation for maintenance is on the rise thanks to a shortage of qualified maintenance techs coinciding with a desire for more efficient maintenance, reduced downtime, and the inroads IT is making on the plant floor. Predictive Maintenance and Predictive Analytics are part of almost every conversation in manufacturing these days, and often the words are used interchangeably.

This blog is intended to make the clear distinction between these phrases and put into perspective the benefits that maintenance automation brings to the table for plant management and decision-makers, to ensure they can bring to their plants focused innovation and boost efficiencies throughout them.

Before we jump into the meat of the topic, let’s quickly review the earlier stages of the maintenance continuum.

Reactive and Preventative approaches

The Reactive and Preventative approaches are most commonly used in the maintenance continuum. With a Reactive approach, we basically run the machine or line until a failure occurs. This is the most efficient approach with the least downtime while the machine or line runs. Unfortunately, when the machine or line comes to a screeching stop, it presents us with the most costly of downtimes in terms of time wasted and the cost of machine repairs.

The Preventative approach calls for scheduled maintenance on the machine or line to avoid impending machine failures and reduce unplanned downtimes. Unfortunately, the Preventative maintenance strategy does not catch approximately 80% of machine failures. Of course, the Preventative approach is not a complete waste of time and money; regular tune-ups help the operations run smoother compared to the Reactive strategy.

Predictive Maintenance vs. Predictive Analytics

As more companies implement IIoT solutions, data has become exponentially more important to the way we automate machines and processes within a production plant, including maintenance processes. The idea behind Predictive Maintenance (PdM), aka condition-based maintenance, is that by frequently monitoring critical components of the machine, such as motors, pumps, or bearings, we can predict the impending failures of those components over time. Hence, we can prevent the failures by scheduling planned downtime to service machines or components in question. We take action based on predictive conditions or observations. The duration between the monitored condition and the action taken is much shorter here than in the Predictive Analytics approach.

Predictive Analytics, the next higher level on the maintenance continuum, refers to collecting the condition-based data over time, marrying it with expert knowledge of the system, and finally applying machine learning or artificial intelligence to predict the event or failure in the future. This can help avoid the failure altogether. Of course, it depends on the data sets we track, for how long, and how good our expert knowledge systems are.

So, the difference between Predictive Maintenance and Predictive Analytics, among other things, is the time between condition and action. In short, Predictive Maintenance is a stepping-stone to Predictive Analytics. Once in place, the system monitors and learns from the patterns to provide input on improving the system’s longevity and uptime. Predictive Maintenance or Preventative Maintenance does not add value in that respect.

While Preventative Maintenance and Predictive Maintenance promises shorter unplanned downtimes, Predictive Analytics promises avoidance of unplanned downtime and the reduction of planned downtime.

The first step to improving your plant floor OEE is with monitoring the conditions of the critical assets in the factory and collecting data regarding the failures.

Other related Automation Insights blogs:

Condition Monitoring & Predictive Maintenance: Cost-Benefit Tradeoffs

In a previous blog post, we discussed the basics of the Potential-Failure (P-F) curve, which refers to the interval between the detection of a potential failure and occurrence of a functional failure. In this post we’ll discuss the cost-benefit tradeoffs of various maintenance approaches.

In general, the goal is to maximize the P-F interval, which is the time between the first symptoms of impending failure and the functional failure taking place. In other words, you want to become aware of an impending failure as soon as possible to allow more time for action. This, however, must be balanced with the cost of the methods of prevention, inspection, and detection.

There is a trade-off between the cost of systems to detect and predict the failures and how soon you might detect the condition. Generally, the earlier the detection/prediction, the more expensive it is. However, the longer it takes to detect an impending failure (i.e. the more the asset’s condition degrades), the more expensive it is to repair it.Every asset will have a unique trade-off between the cost of failure prevention (detection/prediction) and the cost of failure. This means some assets probably call for earlier detection methods that come with higher prevention costs like condition monitoring and analytics systems due to the high cost to repair (see the Prevention-1 and Repair-1 curves in the Cost-Failure/Time chart). And some assets may be better suited for more cost-efficient but delayed detection or even a “run-to-failure” model due to lower cost to repair (the Prevention-2 and Repair-2 curves in the Cost-Failure/Time chart).

There are four basic Maintenance approaches:

Reactive

The Reactive approach has low or even no cost to implement but can result in a high repair/failure cost because no action is taken until the asset has reached a fault state. This approach might be appropriate when the cost of monitoring systems is very high compared to the cost of repairing or replacing the asset. As a general guideline, the Reactive approach is not a good strategy for any critical and/or high value assets due to their high cost of a failure.

Reactive approaches:

- - Offer no visibility
  - Fix only if it breaks – low overall equipment effectiveness (OEE)
  - High downtime
  - Uncertainty of failures

Preventative

The Preventative approach (maintenance at time-based intervals) may be appropriate when failures are age related and maintenance can be performed at regular intervals before anticipated failures occur. Two drawbacks to this approach are: 1) the cost and time of preventative maintenance can be high; and 2) studies show that only 18% of failures are age related (source: ARC Advisory Group). 82% of failures are “random” due to improper design/installation, operator error, quality issues, machine overuse, etc. This means that taking the Preventative approach may be spending time and money on unnecessary work, and it may not prevent expensive failures in critical or high value assets.

Preventative approaches:

- - Scheduled tune ups
  - Higher equipment longevity
  - Reduced downtime compared to reactive mode

Condition-Based

The Condition-Based approach attempts to address failures regardless of whether they are age-based or random. Assets are monitored for one or more potential failure indicators, such as vibration, temperature, current/voltage, pressure, etc. The data is often sent to a PLC, local HMI, special processor, or the cloud through an edge gateway. Predefined limits are set and alerts (alarm, operator message, maintenance/repair) are only sent when a limit is reached. This approach avoids unnecessary maintenance and can give warning before a failure occurs. Condition-based monitoring can be very cost-effective, though very sophisticated solutions can be expensive. It is a good solution when the cost of failure is medium or high and known indicators provide a reliable warning of impending failure.

Condition-based approaches:

- - Based on condition (PdM)
  - Enables predictive maintenance
  - Improves OEE, equipment longevity
  - Drastically reduces unplanned downtime

Predictive Analytics

Predictive Analytics is the most sophisticated approach and attempts to learn from machine performance to predict failures. It utilizes data gathered through Condition Monitoring, and then applies analysis or AI/Machine Learning to uncover patterns to predict failures before they occur. The hardware and software to implement Predictive Analytics can be expensive, and this method is best for high-value/critical assets and expensive potential failures.

Predictive Analytics approaches:

- - Based on patterns – stored information
  - Based on machine learning
  - Improves OEE, equipment longevity
  - Avoids downtime

Each user will need to evaluate the unique attributes of their assets and decide on the best approach and trade-offs of the cost of prevention (detection of potential failure) against the cost of repair/failure. In general, a Reactive approach is only best when the cost of failure is very low. Preventative maintenance may be appropriate when failures are clearly age-related. And advanced approaches such as Condition Based monitoring and Predictive Analytics are best when the cost of repair or failure is high.

Also note that technology providers are continually improving condition monitoring and predictive solutions. By lowering condition monitoring system costs and making them easier to set up and use, users can cost-effectively move from Reactive or Preventative approaches to Condition-Based or Predictive approaches.

Project Uptime – Pay Me Now or Pay Me Later

Back when I worked in the tier 1 automotive industry we were always trying to find time to break into our production schedule to perform preventative maintenance. The idea for this task was to work on the assembly machines or weld cells to improve sensor position, sensor and cable protection and of course clean the machines. As you all know this is easier said than done due to unplanned downtime or production schedule changes, for example. As hard as it is to find time for PM’s (preventative maintenance) it is a must to stay ahead and on top of production. PM’s will not only increase the production time, but it will also help maintain better quality parts by producing less scrap and machine downtime due damaged sensors or cables.

If you have read any of my previous posts you have probably noticed that I refer to the “pay me now or pay me later” analogy. This subject would fall directly into this category, you have to take the time to prevent machine crashes and damaged sensors and cables on the front side rather than being reactive and repairing them when they go down. It has been proven that a properly bunkered or protected proximity sensor will outlast the machine tooling when best practices are executed. It’s important to take the time and look at the way a sensor is mounted or protected or acknowledge when a cable is routed in harm’s way.

PM’s should be an important task that is part of a schedule and followed through in any factory automation or tier 1 production facility. In some cases I have seen where there is a complete bill of material (BOM) or list of tasks to accomplish during the PM time. This list will help maintenance personnel and engineering know what to look for and what are the hot spots that create unplanned downtime. This list could also indicate some key sensors, mounting brackets and high durability cables that can improve the process.

For more information on a full solution supplier or products that can improve and decrease downtime click here.