Maintenance Matters

A holistic approach to maintenance can pay dividends in business performance for quarry operators

By Phil Burge, communications manager for SKF

At one time it was common practice to wait for equipment to fail before repairs were carried out. Maintenance was often viewed as an expensive necessity, requiring skilled labour and machine downtime. Then came preventative and predictive maintenance programmes, which spawned a new generation of diagnostic tools and software designed to allow plant engineers to forward-plan plant shutdown and repair.

The condition-monitoring technology used for detecting problems has developed steadily in recent years, with a broad range of diagnostic equipment and software now available for monitoring machine performance both on site and remotely. Perhaps more importantly, the dawn of these innovative devices has allowed engineers to manage efficiently the frequency and type of maintenance carried out in order to achieve optimum performance across quarries.

Furthermore, these latest instruments have been designed to offer exceptionally high levels of accuracy from compact and often hand-held units that can also be used in wireless networks for remote interrogation and control, providing a significant amount of data that can be used for both short-term maintenance planning and long-term plant cost control.

For example, the latest sensors or accelerometers used for vibration monitoring take advantage of innovative piezoelectric or piezoceramic technology. This provides a robust and reliable method of measuring both high and low frequencies, with low hysteresis characteristics and excellent levels of accuracy over a wide temperature range. In addition, to protect these devices against the ingress of moisture, dust, oils and other contaminants so often present in quarries, they can be packaged in compact stainless steel housings.

Accelerometers are typically mounted in a number of key locations on the equipment to be monitored, with output data either being read periodically using sophisticated hand-held data-collection devices for immediate analysis or subsequent downloading to a PC, or being routed via switch boxes to centralized or higher-level systems for continuous monitoring.

The latest hand-held devices, for example, simplify machine maintenance and often feature particularly easy-to-use ergonomic designs, such as that of SKF’s Machine Condition Advisor (MCA). This innovative unit measures velocity vibration signals from machinery and automatically compares them to pre-programmed ISO guidelines, while simultaneously applying the industry proven SKF Enveloped Acceleration technique to compare readings with established bearing vibration guidelines.

If the measurements exceed these guidelines, the unit display shows an alert or danger alarm to indicate potential bearing damage. Additionally, the MCA features a temperature sensor to check bearing temperatures for abnormal heat loads that could indicate lubrication problems.

Similarly, the latest field-mounted, wireless condition-monitoring measuring devices, such as the Multilog WMx from SKF, have been developed to form one of the key components in advanced condition- monitoring systems, collecting acceleration, velocity, displacement, temperature and bearing condition data.

Typically, this is automatically uploaded for fast and simple data analysis in a condition -monitoring software suite. Moreover, they are ideal for monitoring hazardous, remote or hard to access areas, as they do not rely on wires or cables.

Equally important is the introduction of a number of powerful software tools for improving data collection and analysis, which can be run on both hand-held and centralized computer systems. For example, the SKF @ptitude platform allows detailed information on the condition of the equipment being monitored to be viewed, analyzed and communicated throughout an operation both simply and quickly.

Additionally, users have access to a wealth of online information and periodic results against which to compare recorded data. This can result in improved efficiency levels as labour-intensive data analysis is replaced with an automated process that identifies the probability of specific faults within the machine and then prescribes action.

While all of these developments in condition-monitoring technology represent a real opportunity for cutting the cost and time needed for maintenance tasks, predictive maintenance remains largely a reactive process that, in today’s highly competitive global economy, adds little to a company’s bottom line. What is needed is a holistic approach that optimizes the efficiency of plant and equipment, by proactively managing both system reliability and risk assessment across an entire organization.

This emerging strategy is known as Asset Efficiency Optimization (AEO) and is being developed as a tool to improve plant productivity and thus profitability. AEO creates a dynamic programme that combines the advantages of traditional techniques with procedures that identify the root causes of machine and process problems, and empowers front-line operators to own their machinery, identifying and communicating information to a plant-wide team to maximize uptime.

A successful AEO programme essentially consists of four key elements: maintenance strategy; work identification; work control; and work execution. Although all four elements should ideally be carried out simultaneously for maximum effect, the individual stages can be approached consecutively according to time, money or resource constraints.

The first process in the programme, maintenance strategy, is the stage at which a business sets out its larger goals and objectives, assesses plant criticality and risk, and decides what the most important issues and priorities are. This is essential for a suitable and effective maintenance plan to be created, and sets in place a recognized and auditable company asset-management strategy, which can be easily communicated throughout the organization.

This information can then be used in the second stage, work identification, where critical plant information is gathered and analyzed, allowing informed decisions to be made and the corrective maintenance operations to be carried out.

At this stage an Industrial Decision Support System (iDSS) can provide valuable support to senior maintenance engineers, by making available online relevant condition-based maintenance recommendations, as well as access to specific expert knowledge on asset maintenance. Work requests can then be submitted to a Computerized Maintenance Management System (CMMS), to be combined with other predetermined planned and corrective maintenance activities.

The third stage, work control, relies heavily on the priorities and structure determined during stages one and two, allowing maintenance activities to be planned in detail and scheduled, with tasks prioritized, which takes into account timescales, man hours required, data feedback and competence requirements. Effective planning at this stage, combined with good spares management, well-defined job plans and trained staff, allows resources to be utilized in the most efficient and productive way.

With these three components fully completed, the final stage, work execution, can be implemented, with detailed plans put into action and maintenance work being done. It is crucial that feedback is collected via post-maintenance testing in order for continuous improvement to be maintained and maximum return on investment to be achieved.

Implementing a holistic, inclusive approach to quarry maintenance and asset optimization is vital if companies are to reduce maintenance costs and minimize unexpected downtime. Furthermore, it can improve significantly the efficiency of the plant and equipment, and establish effective lines of communication throughout the entire business.

Overall, this is a sustainable approach to plant maintenance that can have a considerable long-term effect on a company’s performance and profitability.