Predictive Maintenance

2009/2013

Interesting Facts About the Railroad Industry

By |September 20th, 2013|Categories: Predictive Analytics, Predictive Maintenance, Railroad|Tags: |

There are 7 Class I railroads in the US: BNSF Railway, CSX Transportation, Grand Trunk Corporation, Kansas City Southern Railway, Norfolk Southern Combined Railroad Subsidiaries, Soo Line Corporation, and Union Pacific Railroad.

Unlike trucks, barges, and airlines in the U.S., freight railroads do not strain the public purse. Privately owned railroads have spent $525 billion since 1980 building, maintaining and growing their 140,000-mile rail network. That amount equals 40 cents of every revenue dollar. Even during […]

1309/2013

Predictive Maintenance Is More Than Maintenance

By |September 13th, 2013|Categories: Predictive Analytics, Predictive Maintenance|Tags: , |

Traditionally, predictive maintenance is used solely as a maintenance management tool. In most cases, this use is limited to preventing failures. Although this function is important, predictive maintenance can provide substantially more benefits by expanding the scope or mission of the program.

As a maintenance management tool, predictive maintenance can and should be used as a maintenance optimization tool. The program’s focus should be on:

1) Eliminating unnecessary downtime, both scheduled and unscheduled

2) Eliminating unnecessary preventive and corrective maintenance tasks

3) Extending […]

608/2013

We Like Raw Data

By |August 6th, 2013|Categories: Predictive Analytics, Predictive Maintenance|

One of the most complicated and time-consuming components of predictive analytics is managing secondary Asset Operational and Maintenance data to get it in a format that is amenable to predictive analytics. Predikto deals with secondary data exclusively. By secondary, we mean that the data comes from our clients’ EAM / CMMS / Operational Indicators data storage and we have no control over how the data was collected and organized historically. ERP, EAM, CMMS, and other […]

2607/2013

Don’t forget about operational system variables when evaluating maintenance

By |July 26th, 2013|Categories: Predictive Analytics, Predictive Maintenance|Tags: |

A limited amount of effort is made to determine the influence of system variables, like load, speed, product, or instability on the individual component failures analysis. These variations in process variables are often the root-cause of the observed mechanical problem. Unless analysts consider these variables, they will not be able to determine the true root-cause. Instead, they will make recommendations to correct the symptom (e.g., damaged bearing, misalignment), rather than the real problem.

Predikto takes into consideration historical maintenance (PM […]

1904/2013

Techniques for Predictive Maintenance

By |April 19th, 2013|Categories: Predictive Analytics, Predictive Maintenance|

Predictive maintenance techniques help determine the condition of in-service equipment in order to predict when maintenance should be performed. This maintenance technique offers cost savings typical time based preventive maintenance.

The main value of Predicted Maintenance is to prevent unexpected equipment failures while maximizing resources. The key is “the right information at the right time”. By knowing which equipment needs maintenance, maintenance work can be better planned (spare parts, people etc.) and what would have been […]

2203/2013

$57 Trillion in infrastructure will be needed by 2030

By |March 22nd, 2013|Categories: Internet Of Things (IoT), Predictive Maintenance|Tags: , |

McKinsey Global Institute released a study this year stating that the world will need $57 Trillion (with a T) infrastructure projects by 2030.  That spending is just meant to repair our existing infrastructure, which is in a fairly abysmal state in the U.S.

The most recent “report card” from the American Society of Civil Engineers gives the U.S.’s infrastructure a “D” overall and estimates that we need to spend $2.2 trillion over the next five years […]

2203/2013

Replace intuition with predictions

By |March 22nd, 2013|Categories: Predictive Maintenance|Tags: , , |

Many industrial manufacturing companies run a reactive maintenance organization. That means they wait for things to break before replacing parts. Studies have proven that running a preventive maintenance plan reduces expenses by up to 75% in the long run.  The challenge is that many of the Preventive Maintenance schedules were developed on intuition.  Changing the oil filter once a year is enough.  Most organizations are sitting on a wealth of data like prior downtimes (failures), […]