Industrial Machinery Failure Types and Implications for Maintenance Approaches

Industrial machinery can fail in many different ways and for many different reasons. For critical and/or expensive equipment, it is a major challenge to find a way to detect potential failures before they happen and to take action to prevent or minimize the effects. Closely tied to this is the tradeoff between the cost of detection and the cost of failure. We discussed some of these tradeoffs in the blog “Condition Monitoring & Predictive Maintenance: Cost-Benefit Tradeoffs.”

When assessing how equipment might fail, several industry studies* have identified six primary failure types which may be considered:

    • Type A: Lower probability of failure in early- and mid-life of the asset, with a dramatic increase in probability of failure in late-life. This is typical for mechanical devices, such as engines, fans, compressors, and machines.
    • Type B: Higher initial probability of failure when the asset is new, with a much lower/steady failure probability over the rest of the asset’s life. This is often the profile for electronic devices such as computers, PLCs, etc.
    • Type C: Lower initial probability of failure when the asset is new, with an increase to a steady failure probability in mid- and late-life. These are often devices with high stress work conditions, such as pressure relief valves.
    • Type D: Consistent probability of failure throughout the asset life, similar failure probability in early-, mid- and late-life. This can cover many types of industrial machines, often with stable, proven design and components.
    • Type E: Higher probability of failure in early- and late-life, a lower and constant probability of failure in mid-life (often called a “bathtub curve”). This can be devices that “settle in” after a wear-in period and then experience higher failures at the end of life, such as bearings.
    • Type F: Lower probability of failure when new, with a gradual increase over time and without the steep increase in failure probability at the end of life of Type A. This is often typical where age-based wear is steady and gradual in equipment such as turbine engines and structural components (pressure vessels, beams, etc.).

Age-related and non-age-related failures

These six failure types fall into two categories: age-related and non-age-related failures. The studies show that 15-30% of failures are age-related (Types A, E & F) and 70-85% of failures are non-age-related (Types B, C & D). The age-related failures have a clear correlation between the age of the asset and the likelihood of failure. In these cases, preventative maintenance at regular time-based intervals may be appropriate and cost-effective. The non-age-based failures are more “random,” due to improper design/installation, operator error, quality issues, machine overuse, etc. In these cases, preventative maintenance will likely not prevent failure and may waste time and money on unnecessary maintenance.

Table is based on data from studies conducted by United Airlines (1978), Broberg (1973), U.S. Navy (1993 MSDP) and U.S. Navy (2001 SUBMEPP) and ARC Consulting

The fact that approximately 80% of failures are non-age-related has major implications for manufacturers trying to decide on a maintenance approach. The traditional preventative-maintenance approach is not likely to address these failures and may even cause failures when improperly done. It is therefore important to consider a more proactive approach, such as condition-based monitoring or predictive maintenance, especially for assets that are critical to the process and/or expensive.

Preventative maintenance and regular inspection may be a good approach for assets more likely to experience age-based failures in Types A, E, and F. These include fans, bearings, and structural components – and in many cases, the cost of condition monitoring or predictive maintenance may not be worth the cost. But for critical components or equipment, such as bearings on an expensive milling machine or transfer line, it may be worthwhile to apply condition monitoring or predictive maintenance.

And when the assets are more likely to experience non-age-related failures (Types B, C, and D), the proactive approaches are better. Many industrial machines and industrial control/motion components fall into this category, and condition monitoring or predictive maintenance can significantly reduce preventative maintenance costs and unplanned failures while improving machine uptime and Overall Equipment Effectiveness.

You can use this information to improve your maintenance operations. Start by considering your maintenance approach(es), especially for your most critical assets:

    • Are they more likely to experience age-related failures or non-age-related failures?
    • Should you change your maintenance approach to be more proactive?
    • What components and indicators should you measure?

We’ll discuss ideas on how to assess your equipment for condition monitoring/predictive maintenance and what you might measure in separate blogs.

* Studies conducted by United Airlines (1978), Broberg (1973), U.S. Navy (1993 MSDP) and U.S. Navy (2001 SUBMEPP)

Condition Monitoring & Predictive Maintenance: Addressing Key Topics in Packaging

A recent study by the Packaging Machinery Manufacturers Institute (PMMI) and Interact Analysis takes a close look at packaging industry interest and needs for Condition Monitoring and Predictive Maintenance. Customer feedback reveals interesting data on packaging process pain points and the types of machines and components which are best monitored, the data which should be gathered, current maintenance approaches, and the opportunity for a better way: Condition Monitoring and Predictive Maintenance.

What keeps customers awake at night?

The PMMI survey indicates that form, fill & seal machines are very critical to packaging processes and more likely to fail than many other machines. Also critical to the process and a common failure point are filling & dosing machines, and labeling machines.

These three categories of machines are in use in primary packaging and are often the key components in the production line; the downstream processes are usually less critical. They often process a lot of perishable products at high speeds, therefore, any downtime is a big problem for overall equipment effectiveness (OEE), quality, and profitability.

In terms of the components on these machines that are most likely to fail, the ones are pneumatic systems, gearboxes, motors/drives, and sensors.

How can customers reduce unplanned downtime and improve OEE?

Our data shows that the top customer issue is unplanned machine breakdowns, but many packaging firms use reactive or preventative maintenance approaches, which may not be effective for most failures. An ARC study found that only about 20% of failures are age-related. The 80% of failures that are non-age-related would likely not be addressed by reactive or preventative maintenance programs.

A better way to address these potential failures is to monitor the condition of critical machines and components. Condition monitoring can provide early detection of machine deterioration or impending failure and the data can be used for predictive maintenance. Many “smart sensors” can now measure vibration, temperature, humidity, pressure, flow, inclination, and many other attributes which may be helpful in notifying users of emerging problems. And some of these “smart sensors” can also “self-monitor” and help alert users to potential failures in the sensor itself.

What are packaging customers actually doing?

The good news is that the packaging industry is moving forward to find a better way and users understand that Condition Monitoring/Predictive Maintenance gives them the opportunity to prevent unplanned failures, reduce unplanned downtime, and improve OEE, quality and profitability. About 25% of customers have already implemented some sort of Condition Monitoring / Predictive Maintenance, while about 20% are piloting it and 30% plan to implement it. This means that 75% of customers are very interested in Condition Monitoring/Predictive Maintenance, by far the most interest in any technology discussed in the PMMI survey.

Where do you start?

    • Look for the machines which cause you the most frustration. PMMI identified form, fill & seal, filling & dosing, and labeling machines, but there are other machines, including bottling, cartoning, and case/tray handling, that could fail and cause production downtime or damaged product.
    • Consider where, when, and how equipment can fail. Look to your own experience, ask partners with similar machines or perhaps the equipment supplier to help you determine the most common failure points and modes.
    • Analyze which parts of the machine fail. Moving parts are usually the highest potential failure point. On packaging machines, these include motors, gearboxes, fans, pumps, bearings, conveyors, and shafts.
    • Consider what to measure. Vibration is common, and often assessed in combination with temperature and humidity. On some machines, pressure, flow, or amperage/voltage should be measured.
    • Determine the most appropriate maintenance program for each machine. Consider the costs/benefits of reactive, preventative, condition-based monitoring or predictive approaches. In some cases, it may be OK to let a non-critical, low-value asset “run-to-failure,” while in other cases it might be worth investing in Condition Monitoring or Predictive Maintenance to prevent a critical machine’s costly failure.
    • Start small by implementing condition monitoring on one or two machines, and then scaling up once you’ve learned what does and doesn’t work. Using a low-cost sensor, which can be easily integrated with existing controls architectures or added on externally, is also a great way to start.

Condition Monitoring and Predictive Maintenance offer packaging firms a “better way” to address key topics including machine downtime, failures, and OEE. Users can move from a reactive to a proactive maintenance approach by monitoring attributes such as vibration and temperature on critical machines and then analyzing the data. This will allow them to detect and predict potential failures before they become critical, and thereby, reduce unplanned downtime, improve OEE, and save money.

Tackle Quality Issues and Improve OEE in Vision Systems for Packaging

Packaging industries must operate with the highest standards of quality and productivity. Overall Equipment Effectiveness (OEE) is a scoring system widely used to track production processes in packaging. An OEE score is calculated using data specifying quality (percent of good parts), performance (performance of nominal speed) and equipment availability (percent of planned uptime).

Quality issues can directly impact the customer, so it is essential to have processes in place to ensure the product is safe to use and appropriately labeled before it ships out. Additionally, defects to the packaging like dents, scratches and inadequate labeling can affect customer confidence in a product and their willingness to buy it at the store. Issues with quality can lead to unplanned downtime, waste and loss of productivity, affecting all three metrics of the OEE score.

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Traditionally, visual inspections and packaging line audits have been used to monitor quality, however, this labor can be challenging in high volume applications. Sensing solutions can be used to partly automate the process, but complex demands, including multiple package formats and product formulas in the same line, require the flexibility that machine vision offers. Machine vision is also a vital component in adding traceability down to the unit in case a quality defect or product recall does occur.

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Vision systems can increase productivity in a packaging line by reducing the amount of planned and unplanned downtime for manual quality inspection. Vision can be reliably used to detect quality defects as soon as they happen. With this information, a company can make educated improvements to the equipment to improve repeatability and OEE and ensure that no defective product reaches the customers’ hands.

Some vision applications for quality assurance in packaging include:

  • Label inspection (presence, integrity, print quality, OCV/OCR)
    • Check that a label is in place, lined up correctly and free of scratches and tears. Ensure that any printed graphics, codes and text are legible and printed with the expected quality. Use a combination of OCR (Optical Character Recognition) to read a lot number, expiration date or product information, and then OCV (Optical Character Verification) to ensure legibility.
  • Primary and secondary packaging inspection for dents and damage
    Inspect bottles, cans and boxes to make sure that their geometry has not been altered during the manufacturing process. For example, check that a bottle rim is circular and has not been crushed so that the bottle cap can be put on after filling with product.
  • Safety seal/cap presence and position verification
    Verifying that a cap and/or seal has been placed correctly on a bottle, and/or that the container being used is the correct one for the formula / product being manufactured.
  • Product position verification in packages with multiple items
    In packages of solids, making sure they have been filled adequately and in the correct sequence. In pharmaceutical industries, this can be used to check that blister packs have a pill in each space, and in food industries to ensure that the correct food item is placed in each space of the package.
  • Certification of proper liquid level in containers
    For applications in which it can’t be done reliably with traditional sensing technologies, vision systems can be used to ensure that a bottle has been filled to its nominal volume.

The flexibility of vision systems allows for addressing these complex applications and many more with a well-designed vision solution.

For more information on Balluff vision solutions and applications, visit www.balluff.com.

Operational Excellence – How Can We Apply Best Practices Within the Weld Shop?

Reducing manufacturing costs is absolutely a priority within the automotive manufacturing industry. To help reduce costs there has been and continues to be pressure to lower MRO costs on high volume consumables such as inductive proximity sensors.

Traditionally within the MRO community, the strategy has been to drive down the unit cost of components from their suppliers year over year to ensure reduce costs as much as possible. Of course, cost optimization is important and should continue to be, but factors other than unit cost should be considered. Let’s explore some of these as it would apply to inductive proximity sensors in the weld shop.

Due to the aggressive manufacturing environment within weld cell, devices such as inductive proximity sensors are subjected to a variety of hostile factors such as high temperature, impact damage, high EMF (electromagnetic fields) and weld spatter. All of these factors drastically reduce the life of these devices.

There are  manufacturing costs associated with a failed device well beyond that of the unit cost of the device itself. These real costs can be and are reflected in incremental premium costs such as increased downtime (both planned and unplanned),  poor asset allocation, indirect inventory, expedited freight, outsourcing costs, overtime, increased manpower, higher scrap levels, and sorting & rework costs. All of these factors negatively affect a facility’s Overall Equipment Effectiveness (OEE).

Root Cause

In selection of inductive proximity sensors for the weld manufacturing environment there are root cause misconceptions and poor responses to the problem. Responses include: leave the sensor, mounting and cable selection up to the machine builder; bypass the failed sensor and keep running production until the failed device can be replaced; install multiple vending machines in the plant to provide easier access to spare parts (replace sensors often to reduce unplanned downtime);  and the sensors are going to fail anyway so just buy the cheapest device possible.

None of these address the root cause of the failure. They mask the root cause and exacerbate the scheduled and unscheduled downtime or can cause serious part contamination issues down stream, resulting in enormous penalties from their customer.

So, how can we implement a countermeasure to help us drive out these expensive operating costs?

  • Sensor Mounting – Utilize a fixed mounting system that will allow a proximity sensor to slide into perfect mounting position with a positive stop to prevent the device from being over extended and being struck by the work piece. This mounting system should have a weld spatter protective coating to reduce the adherence of weld spatter. This will also provide extra impact protection and a thermal barrier to further assist in protecting the sensing device asset.
  • The Sensor – Utilize a robust fully weld protective coated stainless steel body and face proximity sensor. For applications with the sensor in an “on state” during the weld cycle and/or to detect non-ferrous utilize a proper weld protective coated Factor 1 (F1) device.
  • Cabling – A standard cable will not withstand a weld environment such as MIG welding. Even a cable with protective tubing can have open areas vulnerable for weld berries to land and cause burn through on the cables resulting in a dead short. A proper weld sensor cord set with protective coating on the lock nut, high temp rated and weld resistant overmold to a weld resistant jacketed cable should be used.

By implementing a weld best practice total solution as described above, you will realize significant increases in your facilities OEE contributing to the profitability and sustainability of your organization.

Ask these 3 simple questions:

1) What is the frequency of failure

2) What is the Mean Time To Repair (MTTR)

3) What is the cost per minute of downtime.

Once you have that information you will know with your own metrics  what the problem is costing your facility by day/month/year. You may be surprised to see how much of a financial burden these issues are costing you. Investing in the correct best practice assets will allow you to realize immediate results to boost your company OEE.