Predictive Maintenance Predicts a Potential Failure Before It Occurs
According to the RCM (Reliability-centered maintenance) definitions, predictive maintenance or PdM avoids potential failures by analyzing the actual condition of a piece of equipment, thus called as well a condition-based maintenance. Compared to preventive maintenance which is planned or time-based and is triggered by events or manufacturer’s recommendations, predictive maintenance predicts a potential failure before it occurs by monitoring the machine during normal operating condition.
The time-based approach of preventive maintenance requires scheduled regular maintenance which may not accurately reflect the exact state of a piece of equipment and could lead to unnecessary maintenance interventions. It is assumed that a piece of equipment has a limited life based on pre-determined number of factors, common for its type. Therefore very often a part can be replaced before its actual failure point.
On the other hand predictive maintenance allows maintenance technicians to anticipate the exact moment of a failure and service the machine only when necessary. The repair can be scheduled at a time when the maintenance activity is most cost-effective and has a minimal impact on the production.
Including predictive maintenance in your maintenance program will result in considerable cost savings and higher system reliability.
What tools and tests does predictive maintenance use to evaluate equipment condition?
Predictive maintenance employs a wide range of techniques, firstly to predict when an equipment failure might occur and secondly to prevent the failure by planning and performing maintenance. The right technique to be chosen depends on the industry and the equipment.
For instance, here are some of the most common predictive maintenance tools, applied in one of our customer’s foundry.
Acoustical Analysis / Measurement
This technique uses ultrasounds, which frequency exceeds 20,000 hertz, to assess the condition of the equipment. Listening to ultrasounds emitted by the machines in operation allows to quickly detect the presence of mechanical defects, leaks or electrical problems. The ultrasound output may also be used to detect leaks. In this case it is necessary to place a transmitter in the vessel that should be tested, and then observe if the ultrasound is picked out, a sign that the tank is not waterproof.
Thermography involves monitoring through temperature sensors (typically, infrared cameras) the thermal profile of the equipment. These devices can detect abnormal changes in temperature, indicating a potential malfunction: hot spots, leaks, electrical faults ... Thermography has the advantage of not requiring any contact with the monitored machines, which avoids interrupting operations.
Vibration analysis is often used in addition to the acoustic or thermal measurement to specify the diagnosis. It consists in analyzing the vibrations emitted by the machine to detect and identify malfunctions. Vibrations’ analysis enables, for example, the detection of clamping errors, misaligned drive shafts or damaged belts. Various devices, generally non-intrusive, as accelerometers (acceleration sensors or rotation), laser vibrometers, digital recorders or spectrum analyzers, pick up the vibrations and measure them according to amplitude, noise level (dB) or frequency (Hz).
Other PdM techniques
Different techniques can be combined in order to multiply the accuracy of predictions. Measures may also be supplemented with data from many other sources of information: energy consumption of machines, production analysis, fluid analysis or issued oils, spectral analysis...
Why is preventive maintenance more common than predictive maintenance?
Preventive maintenance is more common because it is easier to implement and less expensive than predictive maintenance.
Predictive maintenance requires expensive measurement equipment and the intervention of highly qualified technicians to accurately interpret condition-monitoring data and deliver the right information at the right time.
Not all applications are suitable for predictive maintenance. To minimize the upfront cost of a condition-monitoring program, some companies limit it only to applications with a critical operational function.
Why is adopting predictive maintenance worth it?
Although setting up all the resources for a predictive maintenance program might be expensive, this is a cost-efficient strategy in a long-term perspective, reducing total time and cost spent on equipment maintenance. By monitoring a machine during its performance, predictive maintenance detects problems in their very beginning and ensures that a machine is only shut down right before a failure.
This minimizes the production hours lost to maintenance and prevent the occurrence of breakdowns, leading to unplanned downtime of the equipment.
The main advantages of predictive maintenance can be summarized as follow:
Advantages of predictive maintenance over corrective maintenance:
- Fewer failures and thus less downtime
- Better planning of interventions and therefore better team preparation
- Better relations between production and maintenance services
- Effective spare parts management
Advantages of predictive maintenance over systematic preventive maintenance (regular):
- Full-capacity use of the entire equipment
- Decreased spare parts inventory
- Improved equipment monitoring, which enables to fix operator errors or minor incidents, which may cause more serious failures
- Improved safety, by detecting incidents in time (such as heating), which may evolve into more serious incidents
How to implement predictive maintenance thanks to Mobility Work?
Efficiently implementing predictive maintenance for a plant with tens of thousands pieces of equipment is not easy. Thanks to Mobility Work CMMS, you can easily ensure that the right data is sent to the right piece of equipment to trigger maintenance planning and execution.
For example, if you are using a data recovery software with sensors, you can forward the collected information to Mobility Work, where all monitoring data will be stored and analysed in near real time by the Moblity Work analytic tool. Based on the results, maintenance activities will be automatically scheduled and launched.
As a community-based tool, Mobility Work has become the first social maintenance network, with 121 800 registered pieces of equipment and 212 273 registered maintenance operations. It provides an important database to which every single member of our community can have access in order to collect the information needed to get ahead in today’s increasingly connected world.
Proper equipment maintenance results in an increased operational stability and decreased unplanned equipment downtime. Predictive maintenance is a crucial component of every lean equipment maintenance program. The proper adoption of predictive maintenance techniques promises significant cost savings over conditional or time-based preventive maintenance, because tasks are performed only when really needed.
Thanks to the interaction between predictive maintenance analytics and big data, critical assets can be continuously monitored, diagnosed and much better serviced.