Transit Asset Management System 49 U.S.C. 5326 as part of the MAP21

Transit provides more than 10 billion passenger trips each year, which represents more trips each month than all of the Nation’s airlines combined will make in a year. When transit assets are not in a state of good repair (SGR), the consequences often include increased safety risks, decreased reliability, higher maintenance costs, and an overall lower quality of service to customers.

The Moving Ahead for Progress in the 21st Century Act, MAP-21, states in its section 5326 that the Federal Transit Administration (FTA) will implement a national transit asset management system, including “a strategic and systematic process of operation, maintaining, and improving public transportation capital assets effectively throughout the life cycle of such assets.”

The Transit Asset Management System will include at minimum:

  1. The definition of the ‘state of good repair’, that should include standards for measuring the condition of equipment, infrastructure, rolling stock, and facilities of the capital assets of the public transportation systems that receive FTA funding.
  2. Federal financial assistance to develop a transit asset management plan.
  3. Reports of the conditions of the systems, and any changes to it.
  4. An analytical process or decision support tool that allows for the estimation of capital investment needs, and the prioritization of the public transportation systems.
  5. Technical assistance to the FTA funding recipients.

Predictive Analytics tools for Asset Management can help to meet the requirements of the Transit Asset Management systems, 49 U.S.C. 5326, especially those points related to the implementation of a national transit asset management system, that include an analytical process or decision support tool for use by public transportation systems.

Uptime Magazine – Can Your Machine Tell You When It Will Fail In The Future?

Predikto will be featured in Uptime Magazine  Feb 2014 issue with an article about Predictive Analytics called “Can Your Machine Tell You When It Will Fail In The Future?”.

The mission of Uptime Magazine is to make maintenance reliability professionals and asset managers safer and more successful by providing case studies, tutorials, practical tips, news, book reviews, and interactive content.

To view the article, click on the image below.


M2M Analytics Will Generate $14 Billion by 2018

Senior analyst, Aapo Markkanen, from ABI Research forecasts that the M2M analytics and big data industry will grow 53.1% over the next 5 years from US $1.9 billion in 2013 to US $14.3 billion in 2018.

The forecast includes revenue segmentation for the five components that together enable analytics to be used in M2M services:
1) Data integration
2) Data storage
3) Core analytics
4) Data presentation
5) Associated professional services

Predikto is uniquely positioned to make an impact in this transformational trend. Predikto is not a consulting company. Our solutions harness the power of Predictive Analytics to address operations challenges in asset intensive industries. We use our clients data and turn it into actionable insights to reduce asset failures, predict production yields, and improve operational performance. Our solutions enable clients to perform an action.

Predicting Production Delays and Expected Yields in Future Batches

Predikto will be releasing a new Advanced Analytics solution that will enable operators at continuous manufacturing facilities to predict production delays, asset failures, and expected production yield for future batches without the need to send us sensor data on a daily basis.

Operators will enter future batch settings like Steel Quality, Billets, Speed, Temperature, and a handful of other information.  Predikto will show in a simple graphical web form the risks embedded in the batch and the recommended settings to maximize yield and reduce the probability of having delays or failures with their equipment.  This could be a game changer. Stay tuned…


If it has sensors and it failed in the past, we can predict future failures!

Predictive Analytics is a proven technology for detecting and diagnosing emerging reliability problems far earlier than traditional methods. It is also a non intrusive method that should not be introduced to complete other predictive maintenance techniques and processes.

Some maintenance experts or operational executives believe it is difficult to predict failures in advance. They are correct that is is difficult, but Predikto makes the process very simple by performing a Proof of Concept on a few key assets without the need to install any software or build automated interfaces.

We love these challenges. We have a simple answer for skeptics: “If it has sensors and it has failed in the past, then we can predict when it will fail in the future”. It all starts by clients giving us some sample data and Predikto shows them what can be predicted in that particular case.

Predictive Maintenance Is More Than Maintenance

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 the useful life of critical systems

4) Reducing the total life-cycle cost of these systems

What is Predictive Analytics

Predictive analytics is an area of statistical analysis that deals with extracting information from data and using it to predict future trends and behavior patterns. The core of predictive analytics relies on capturing relationships between explanatory variables and the predictive variables from past occurrences, and exploiting it to predict future outcomes.

Predictive modeling draws from statistics and optimization techniques to extract accurate information from large volumes of data. Modeling techniques produce interpretable information allowing maintenance personnel to understand the implications of events, enabling them to take action based on these implications.

Predictive analytics builds on prior investments in enterprise asset management (EAM) systems, combines real-time data from sensors and other acquisition techniques with historical data to predict potential asset failures, and enables the move from reactive (scheduled, break-fix) to proactive (condition-based, preventive) maintenance.

Predictive analytics can be used to analyze the real-time data from the sensors in the context of historical data and asset information held in the EAM system to predict future conditions such as faults or failures and produce alarms or schedule maintenance or replacement. Predictive analytics also complement other existing systems such as data historians and SCADA systems.