Tag Archives: Dingo

Dingo’s intelligence asset management platform receives update

Dingo has launched a completely redesigned intelligence™, powered by Trakka®, to, it says, empower customers to gain deeper insights into their asset health programs through data visualisation and reporting.

The release includes improved datasets where the underlying data model of intelligence has been expanded. In this release is the first observation related data. This data is used in the new Oil Cleanliness report, with additional fields are included on most reports, to allow for more filtering. The Oil Cleanliness report allows users to compare fluid particle cleanliness according to ISO 4406: 1999 guidelines, while providing the ability to evaluate and identify contamination entry points, by comparing bulk fluids with in-use fluids. It also allows users to manage and maintain high quality and contaminant free fluids.

All eleven existing reports have been updated to suit the layout and style produced for the two new reports. User interfaces are now intuitive and have been constructed to empower teams to review reports dynamically during meetings, with no data preparation required, Dingo says.

Alongside the Oil Cleanliness report is the newly introduced Benchmarking report, which provides users with the ability to compare component age achieved across sites and operations within the organisation. The report also assesses the statistical plots to increase predictability in when changeouts are occurring. Lastly, users can evaluate potential components for component life extension, or budget life increases.

The full list of reports include:

  • Summary;
  • Asset Health;
  • Component Health;
  • Reviews;
  • Open Actions;
  • Completed Actions;
  • Actions Count;
  • Breakdown Avoidance;
  • Financials;
  • Component Life Achieved;
  • Component Life Savings;
  • Benchmarking; and
  • Oil Cleanliness.

Dingo improves Trakka predictive maintenance capabilities with AI

Dingo says its new Trakka Predictive Analytics solution uses artificial intelligence and machine learning to predict impending equipment failures with confidence, allowing customers to proactively perform corrective maintenance actions to minimise downtime and optimise asset life.

The release comes around five months since the company laid the groundwork for the new solution with an announcement that it would introduce practical machine learning models built using real customer data and targeted at specific industry problems from January.

The new Trakka solution includes a series of sophisticated predictive analytics models to provide anomaly detection and failure prediction for asset intensive industries, the company said. These models are built by uniting failure data from actual equipment, “Dingo’s industry expertise and data science to address common component-specific failure modes, such as final drive gear teeth wear”.

Powered by a proprietary machine-learning library, the Trakka Predictive Analytics solution can, Dingo says, predict the time until asset/component failure with a high degree of accuracy. The company said its customers will reap the benefits of these remaining useful life (RUL) models (pictured) as they:

  • Reduce unexpected failures and downtime;
  • Reduce repair cost as scheduling is optimised;
  • Reduce loss of wasted potential in capital;
  • Reduce unnecessary maintenance activities;
  • Reduce personnel and process risk by creating a safer and more controlled environment;
  • Improve component life by acting earlier;
  • Improve confidence in planning component replacements;
  • Improve equipment availability and reliability;
  • Improve budgeting and the bottom line, and;
  • Improve business related processes such as procurement, logistics and management.

Dingo said: “Before any predictions can be made, Dingo’s domain experts and data science team work with a customer’s historical failure and condition monitoring data to deploy or adapt existing models or create new machine learning models to correctly identify failures within the customer’s fleet.

“This process typically involves data collecting, cleansing and validation to ensure model outputs are as accurate as possible. The transition to online predictive analytics is complete once the data ingestion pipeline is ready and the models are fully trained and tested.”

The predictive models are designed with scalability in mind, Dingo said, meaning they can be easily re-trained to work with a broad range of asset and failure mode problems experienced by real mining operations, making them highly reusable.

“The models are continuously optimised through ongoing validation and the input of new data and equipment performance information,” Dingo said.

And, the platform connects a broad range of systems and software to provide data surrounding asset health, including enterprise resource planning & enterprise asset management systems, computerised maintenance management systems, fleet management systems and all forms of condition monitoring data, including oil analysis, visual inspections, sensor data, vibration and thermography.

Dingo’s new Denver Asset Health Centre to improve predictive maintenance coverage

Dingo Software, a leader in providing predictive maintenance solutions to asset-intensive industries, has opened up its new Asset Health Centre in Denver, Colorado.

The modern, purpose-designed facility will provide operations with asset health coverage 24 hours a day, seven days a week, Dingo says.

“From a sophisticated control room, Dingo analysts will monitor equipment condition, using a proprietary predictive analytics platform enriched with decades of maintenance expertise, to identify impending issues and then prescribe the corrective maintenance actions for immediate resolution,” the company said.

“This actionable intelligence will dramatically improve decision-making and could be the difference between an inexpensive repair and a catastrophic breakdown costing millions in parts and productivity losses.”

Currently managing over $12 billion worth of heavy equipment across six continents, the new centre, combined with its facility in Brisbane, Australia (pictured), will provide operations with a convenient, cost-effective way to monitor and maintain equipment, according to the company.

Dingo CEO Paul Higgins said the Denver team will be able to serve customers remotely using its award-winning, cloud-based predictive maintenance software, Trakka®, and team of Condition Intelligence™ experts. “Right now, around 250 different operations globally are capitalising on Dingo’s remote Asset Health services,” he said.

Safety is another major benefit Dingo will deliver to these operations, according to Higgins.

“When people work on a site they’re exposed to inherent dangers, so being offsite and working from a remote centre is advantageous,” he said. “And well-maintained equipment is always safer. It’s an issue that Dingo and its customers take very seriously.”

Other benefits include access to subject-matter expertise: “Dingo’s team of analysts has deep domain expertise in a range of fields.” Higgins said. “We have people with fixed plant, mobile, and underground experience; experts who understand trucks, shovels, drills—as well as a range of condition monitoring technologies.”

It would be an extremely expensive endeavour for one customer to find, and hire, an equivalent team of experts, according to Dingo.

“We can deliver the right expertise at the right time to help with the right issue,” Higgins said.

Dingo also provides clients with access to its proprietary equipment health database.

“We have one of the largest databases of asset health information in the world,” Higgins said. “Even when a customer has its own in-house team, they may only have access to the data they have generated. With over 25 years’ worth of asset performance data, we know what good looks like and, when equipment is underperforming, we know how to fix it.”

He added: “We’ve developed the unified platform specifically to enable access across multiple mining operations. An example is the senior management asset performance dashboard, which provides a comprehensive view of fleet health to ensure mines are operating at peak performance.”

Ultimately the Asset Health Centre doesn’t function as a complete outsourcing option, according to Dingo.

“Some customers take more of the workload on themselves, others don’t have the resources and want to move quickly so they bring us in,” Higgins said. “We don’t replace the customer’s team, we help them. We filter the noise and streamline decision-making, so they can focus on other, more pressing issues.”

Dingo is expanding the use of practical machine learning models in its Trakka predictive maintenance software with two new releases next year.

Dingo set for big 2019 with new Trakka predictive analytics models

Dingo is to introduce practical machine learning models built using real customer data and targeted at specific industry problems from January, the company has announced.

“Dingo has spent the last 12 months developing and refining machine learning models, in collaboration with Queensland University of Technology, to detect anomalies in condition monitoring data in Dingo’s OEM-independent global asset health database,” the company said.

These models highlight anomalous behaviour in the data and will be available to users of Trakka®. As more quality component failure data is added to the data set, the accuracy of the anomaly detection models will improve, according to Dingo.

“By detecting anomalies automatically, it will allow our users to:

  • “Detect developing issues well before traditional engineering limits are reached;
  • “Find slight changes in data trends, not discernable to a human analyst;
  • “Act faster to correct abnormalities and restore equipment to normal operating condition.”

Dingo says Trakka is a powerful, cloud-based predictive maintenance software system designed to house all of asset health data under one roof. The solution provides operations with the tools, insights, and decision-support to run a best-in-class asset health programme, according to the company.

Further to this announcement, Dingo is also developing sophisticated predictive analytics models aimed at forecasting the remaining useful life of assets. Due for release in June 2019, Trakka users will have access to valuable analytical information about the Probability of Failure and Degradation Indexes, the company said.

“These models are built by Dingo subject matter experts for common asset specific failure modes, eg Engine piston ring wear. They are designed with scalability in mind and can be easily retrained to work with a broad range of asset/failure mode problems experienced by real mining operations, making them highly reusable without further development,” Dingo said.

“By creating an accurate Remaining Useful Life model, it will allow our users to:

  • “More confidently plan component replacements;
  • “Optimise repair costs when components are nearing end of life;
  • “Improve related processes such as budgeting and supply chain logistics and management.”